Discriminative Correlation Filters based tracking algorithms exploiting conventional handcrafted features have achieved impressive results both in terms of accuracy and robustness. Template handcrafted features have shown excellent performance, but they perform poorly when the appearance of target changes rapidly such as fast motions and fast deformations. In contrast, statistical handcrafted features are insensitive to fast states changes, but they yield inferior performance in the scenarios of illumination variations and background clutters. In this work, to achieve an efficient tracking performance, we propose a novel visual tracking algorithm, named MFCMT, based on a complementary ensemble model with multiple features, including Histogram of Oriented Gradients (HOGs), Color Names (CNs) and Color Histograms (CHs). Additionally, to improve tracking results and prevent targets drift, we introduce an effective fusion method by exploiting relative entropy to coalesce all basic response maps and get an optimal response. Furthermore, we suggest a simple but efficient update strategy to boost tracking performance. Comprehensive evaluations are conducted on two tracking benchmarks demonstrate and the experimental results demonstrate that our method is competitive with numerous state-of-the-art trackers. Our tracker achieves impressive performance with faster speed on these benchmarks.
How to combine the complementary capabilities of an ensemble of different algorithms has been of central interest in visual object tracking. A significant progress on such a problem has been achieved, but considering short-term tracking scenarios. Instead, long-term tracking settings have been substantially ignored by the solutions. In this paper, we explicitly consider long-term tracking scenarios and provide a framework, named CoCoLoT, that combines the characteristics of complementary visual trackers to achieve enhanced long-term tracking performance. CoCoLoT perceives whether the trackers …
Given a set of detections, detected at each time instant independently, we investigate how to associate them across time. This is done by propagating labels on a set of graphs, each graph capturing how either the spatio-temporal or the appearance cues promote the assignment of identical or distinct labels to a pair of detections. The graph construction is motivated by a locally linear embedding of the detection features. Interestingly, the neighborhood of a node in appearance graph is defined to include all the nodes for which the appearance feature is available (even if they are temporally di…
Robust visual tracking is a challenging computer vision problem, with many real-world applications. Most existing approaches employ hand-crafted appearance features, such as HOG or Color Names. Recently, deep RGB features extracted from convolutional neural networks have been successfully applied for tracking. Despite their success, these features only capture appearance information. On the other hand, motion cues provide discriminative and complementary information that can improve tracking performance. Contrary to visual tracking, deep motion features have been successfully applied for actio…
Dalian University of Technology（大连理工大学）；Advanced Institute of Information Technology, Peking University（北京大学信息技术高等研究院）
Jointly exploiting multiple different yet complementary domain information has been proven to be an effective way to perform robust object tracking. This paper focuses on effectively representing and utilizing complementary features from the frame domain and event domain for boosting object tracking performance in challenge scenarios. Specifically, we propose Common Features Extractor (CFE) to learn potential common representations from the RGB domain and event domain. For learning the unique features of the two domains, we utilize a Unique Extractor for Event (UEE) based on Spiking Neural Net…
Visual Tracking is a complex problem due to unconstrained appearance variations and dynamic environment. Extraction of complementary information from the object environment via multiple features and adaption to the target's appearance variations are the key problems of this work. To this end, we propose a robust object tracking framework based on Unified Graph Fusion (UGF) of multi-cue to adapt to the object's appearance. The proposed cross-diffusion of sparse and dense features not only suppresses the individual feature deficiencies but also extracts the complementary information from multi-c…
The task of RGBT tracking aims to take the complementary advantages from visible spectrum and thermal infrared data to achieve robust visual tracking, and receives more and more attention in recent years. Existing works focus on modality-specific information integration by introducing modality weights to achieve adaptive fusion or learning robust feature representations of different modalities. Although these methods could effectively deploy the modality-specific properties, they ignore the potential values of modality-shared cues as well as instance-aware information, which are crucial for ef…
State-of-the-art multi-object tracking~(MOT) methods follow the tracking-by-detection paradigm, where object trajectories are obtained by associating per-frame outputs of object detectors. In crowded scenes, however, detectors often fail to obtain accurate detections due to heavy occlusions and high crowd density. In this paper, we propose a new MOT paradigm, tracking-by-counting, tailored for crowded scenes. Using crowd density maps, we jointly model detection, counting, and tracking of multiple targets as a network flow program, which simultaneously finds the global optimal detections and tr…
Technical University Munich（慕尼黑工业大学）；University of Adelaide（阿德莱德大学）；ETH Zürich（苏黎世联邦理工学院）；TU Darmstadt（达姆施塔特工业大学）
Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide the most objective measure of performance and are therefore important guides for research. We present a benchmark for Multiple Object Tracking launched in the late 2014, with the goal of creating a framework for the standardized evaluation of multiple object tracking methods. This paper collects the two releases of the benchmark made so far, and provides an in-depth analysis of almost 50 state-of-the-art trackers tha…
We present our 1st place solution to the Group Dance Multiple People Tracking Challenge. Based on MOTR: End-to-End Multiple-Object Tracking with Transformer, we explore: 1) detect queries as anchors, 2) tracking as query denoising, 3) joint training on pseudo video clips generated from CrowdHuman dataset, and 4) using the YOLOX detection proposals for the anchor initialization of detect queries. Our method achieves 73.4% HOTA on the DanceTrack test set, surpassing the second-place solution by +6.8% HOTA.
In multi-object tracking applications, model parameter tuning is a prerequisite for reliable performance. In particular, it is difficult to know statistics of false measurements due to various sensing conditions and changes in the field of views. In this paper we are interested in designing a multi-object tracking algorithm that handles unknown false measurement rate. Recently proposed robust multi-Bernoulli filter is employed for clutter estimation while generalized labeled multi-Bernoulli filter is considered for target tracking. Performance evaluation with real videos demonstrates the effec…
Fraunhofer Center Machine Learning（弗劳恩霍夫机器学习中心）；Fraunhofer ITWM（弗劳恩霍夫工业数学研究所）；Institute for Machine Learning and Analytics (IMLA) Offenburg University（奥芬堡大学机器学习与分析研究所（IMLA））；University of Mannheim（曼海姆大学）
Multiple Object Tracking (MOT) is a long-standing task in computer vision. Current approaches based on the tracking by detection paradigm either require some sort of domain knowledge or supervision to associate data correctly into tracks. In this work, we present an unsupervised multiple object tracking approach based on visual features and minimum cost lifted multicuts. Our method is based on straight-forward spatio-temporal cues that can be extracted from neighboring frames in an image sequences without superivison. Clustering based on these cues enables us to learn the required appearance i…
RGB-Thermal object tracking attempt to locate target object using complementary visual and thermal infrared data. Existing RGB-T trackers fuse different modalities by robust feature representation learning or adaptive modal weighting. However, how to integrate dual attention mechanism for visual tracking is still a subject that has not been studied yet. In this paper, we propose two visual attention mechanisms for robust RGB-T object tracking. Specifically, the local attention is implemented by exploiting the common visual attention of RGB and thermal data to train deep classifiers. We also in…
University of Electronic Science and Technology of China（电子科技大学）；Polytechnique Montréal（蒙特利尔理工学院）
Recently, the Kernelized Correlation Filters tracker (KCF) achieved competitive performance and robustness in visual object tracking. On the other hand, visual trackers are not typically used in multiple object tracking. In this paper, we investigate how a robust visual tracker like KCF can improve multiple object tracking. Since KCF is a fast tracker, many can be used in parallel and still result in fast tracking. We build a multiple object tracking system based on KCF and background subtraction. Background subtraction is applied to extract moving objects and get their scale and size in combi…
University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；The University of Arizona（亚利桑那大学）
Visual multi-object tracking has the potential to accelerate many forms of quantitative analyses, especially in research communities investigating the motion, behavior, or social interactions within groups of animals. Despite its potential for increasing analysis throughput, complications related to accessibility, adaptability, accuracy, or scalable application arise with existing tracking systems. Several iterations of prototyping and testing have led us to a multi-object tracking system -- ABCTracker -- that is: accessible in both system as well as technical knowledge requirements, easily ad…
Northeastern University（东北大学）；Roux Institute at Northeastern University（东北大学鲁克斯研究所）；Bouve College of Health Sciences, Northeastern University（东北大学布维健康科学学院）
In recent decades, due to the groundbreaking improvements in machine vision, many daily tasks are performed by computers. One of these tasks is multiple-vehicle tracking, which is widely used in different areas such as video surveillance and traffic monitoring. This paper focuses on introducing an efficient novel approach with acceptable accuracy. This is achieved through an efficient appearance and motion model based on the features extracted from each object. For this purpose, two different approaches have been used to extract features, i.e. features extracted from a deep neural network, and…
The University of Hong Kong（香港大学）；Carnegie Mellon University（卡内基梅隆大学）；ByteDance AI Lab（字节跳动人工智能实验室）；Tongji University（同济大学）
In this work, we propose TransTrack, a simple but efficient scheme to solve the multiple object tracking problems. TransTrack leverages the transformer architecture, which is an attention-based query-key mechanism. It applies object features from the previous frame as a query of the current frame and introduces a set of learned object queries to enable detecting new-coming objects. It builds up a novel joint-detection-and-tracking paradigm by accomplishing object detection and object association in a single shot, simplifying complicated multi-step settings in tracking-by-detection methods. On …
University of Western Australia（西澳大利亚大学）；Deakin University（迪肯大学）；University of Central Florida（中佛罗里达大学）
Deep learning-based Multiple Object Tracking (MOT) currently relies on off-the-shelf detectors for tracking-by-detection.This results in deep models that are detector biased and evaluations that are detector influenced. To resolve this issue, we introduce Deep Motion Modeling Network (DMM-Net) that can estimate multiple objects' motion parameters to perform joint detection and association in an end-to-end manner. DMM-Net models object features over multiple frames and simultaneously infers object classes, visibility, and their motion parameters. These outputs are readily used to update the tra…
Object-to-camera motion produces a variety of apparent motion patterns that significantly affect performance of short-term visual trackers. Despite being crucial for designing robust trackers, their influence is poorly explored in standard benchmarks due to weakly defined, biased and overlapping attribute annotations. In this paper we propose to go beyond pre-recorded benchmarks with post-hoc annotations by presenting an approach that utilizes omnidirectional videos to generate realistic, consistently annotated, short-term tracking scenarios with exactly parameterized motion patterns. We have …
Beijing University of Posts and Telecommunications（北京邮电大学）
Multiple Object Tracking (MOT) has rapidly progressed in recent years. Existing works tend to design a single tracking algorithm to perform both detection and association. Though ensemble learning has been exploited in many tasks, i.e, classification and object detection, it hasn't been studied in the MOT task, which is mainly caused by its complexity and evaluation metrics. In this paper, we propose a simple but effective ensemble method for MOT, called EnsembleMOT, which merges multiple tracking results from various trackers with spatio-temporal constraints. Meanwhile, several post-processin…
University of Maryland, College Park（马里兰大学帕克分校）；Harvard Medical School（哈佛医学院）；National Institutes of Health（美国国立卫生研究院）
Current methods in multiple object tracking (MOT) rely on independent object trajectories undergoing predictable motion to effectively track large numbers of objects. Adversarial conditions such as volatile object motion and imperfect detections create a challenging tracking landscape in which established methods may yield inadequate results. Multiple hypothesis hypergraph tracking (MHHT) is developed to perform MOT among interdependent objects amid noisy detections. The method extends traditional multiple hypothesis tracking (MHT) via hypergraphs to model correlated object motion, allowing fo…
Center for Research in Computer Vision, University of Central Florida（中佛罗里达大学计算机视觉研究中心）
Traditionally, object tracking and segmentation are treated as two separate problems and solved independently. However, in this paper, we argue that tracking and segmentation are actually closely related and solving one should help the other. On one hand, the object track, which is a set of bounding boxes with one bounding box in every frame, would provide strong high-level guidance for the target/background segmentation task. On the other hand, the object segmentation would separate object from other objects and background, which will be useful for determining track locations in every frame. …
Gottfried Wilhelm Leibniz Universität Hannover（汉诺威莱布尼茨大学）
Multiple people tracking is a key problem for many applications such as surveillance, animation or car navigation, and a key input for tasks such as activity recognition. In crowded environments occlusions and false detections are common, and although there have been substantial advances in recent years, tracking is still a challenging task. Tracking is typically divided into two steps: detection, i.e., locating the pedestrians in the image, and data association, i.e., linking detections across frames to form complete trajectories. For the data association task, approaches typically aim at dev…
KDDI Research, Inc.（KDDI综合研究所）；Graduate School of Informatics, Nagoya University（名古屋大学信息学研究科）
In this paper, we propose a Multiple Human Tracking method using multi-cues including Primitive Action Features (MHT-PAF). MHT-PAF can perform the accurate human tracking in dynamic aerial videos captured by a drone. PAF employs a global context, rich information by multi-label actions, and a middle level feature. The accurate human tracking result using PAF helps multi-frame-based action recognition. In the experiments, we verified the effectiveness of the proposed method using the Okutama-Action dataset. Our code is available online.
With the recent advances in the object detection research field, tracking-by-detection has become the leading paradigm adopted by multi-object tracking algorithms. By extracting different features from detected objects, those algorithms can estimate the objects' similarities and association patterns along successive frames. However, since similarity functions applied by tracking algorithms are handcrafted, it is difficult to employ them in new contexts. In this study, it is investigated the use of artificial neural networks to learning a similarity function that can be used among detections. D…
Data association-based multiple object tracking (MOT) involves multiple separated modules processed or optimized differently, which results in complex method design and requires non-trivial tuning of parameters. In this paper, we present an end-to-end model, named FAMNet, where Feature extraction, Affinity estimation and Multi-dimensional assignment are refined in a single network. All layers in FAMNet are designed differentiable thus can be optimized jointly to learn the discriminative features and higher-order affinity model for robust MOT, which is supervised by the loss directly from the a…
Multiple object tracking (MOT) is the task containing detection and association. Plenty of trackers have achieved competitive performance. Unfortunately, for the lack of informative exchange on these subtasks, they are often biased toward one of the two and underperform in complex scenarios, such as the inevitable misses and mistaken trajectories of targets when tracking individuals within a crowd. This paper proposes TransFiner, a transformer-based approach to post-refining MOT. It is a generic attachment framework that depends on query pairs, the bridge between an original tracker and TransF…
Nanjing University of Science and Technology（南京理工大学）；Graz University of Technology（格拉茨技术大学）
Existing Multiple Object Tracking (MOT) methods design complex architectures for better tracking performance. However, without a proper organization of input information, they still fail to perform tracking robustly and suffer from frequent identity switches. In this paper, we propose two novel methods together with a simple online Message Passing Network (MPN) to address these limitations. First, we explore different integration methods for the graph node and edge embeddings and put forward a new IoU (Intersection over Union) guided function, which improves long term tracking and handles iden…
ETH Zürich（苏黎世联邦理工学院）；HKUST（香港科技大学）；Kuaishou Technology（快手科技）
Multiple object tracking and segmentation requires detecting, tracking, and segmenting objects belonging to a set of given classes. Most approaches only exploit the temporal dimension to address the association problem, while relying on single frame predictions for the segmentation mask itself. We propose Prototypical Cross-Attention Network (PCAN), capable of leveraging rich spatio-temporal information for online multiple object tracking and segmentation. PCAN first distills a space-time memory into a set of prototypes and then employs cross-attention to retrieve rich information from the pas…
Australian National University（澳大利亚国立大学）；ACRV（澳大利亚机器人视觉中心）；Monash University（莫纳什大学）；CVLab-EPFL（洛桑联邦理工学院计算机视觉实验室）
Despite the recent advances in multiple object tracking (MOT), achieved by joint detection and tracking, dealing with long occlusions remains a challenge. This is due to the fact that such techniques tend to ignore the long-term motion information. In this paper, we introduce a probabilistic autoregressive motion model to score tracklet proposals by directly measuring their likelihood. This is achieved by training our model to learn the underlying distribution of natural tracklets. As such, our model allows us not only to assign new detections to existing tracklets, but also to inpaint a track…
School of Information and Communication Engineering, Xi’an Jiaotong University（西安交通大学信息与通信工程学院）
Due to better video quality and higher frame rate, the performance of multiple object tracking issues has been greatly improved in recent years. However, in real application scenarios, camera motion and noisy per frame detection results degrade the performance of trackers significantly. High-speed and high-quality multiple object trackers are still in urgent demand. In this paper, we propose a new multiple object tracker following the popular tracking-by-detection scheme. We tackle the camera motion problem with an optical flow network and utilize an auxiliary tracker to deal with the missing …
Siamese trackers demonstrated high performance in object tracking due to their balance between accuracy and speed. Unlike classification-based CNNs, deep similarity networks are specifically designed to address the image similarity problem, and thus are inherently more appropriate for the tracking task. However, Siamese trackers mainly use the last convolutional layers for similarity analysis and target search, which restricts their performance. In this paper, we argue that using a single convolutional layer as feature representation is not an optimal choice in a deep similarity framework. We …
Tracking multiple objects in videos relies on modeling the spatial-temporal interactions of the objects. In this paper, we propose a solution named TransMOT, which leverages powerful graph transformers to efficiently model the spatial and temporal interactions among the objects. TransMOT effectively models the interactions of a large number of objects by arranging the trajectories of the tracked objects as a set of sparse weighted graphs, and constructing a spatial graph transformer encoder layer, a temporal transformer encoder layer, and a spatial graph transformer decoder layer based on the …
École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）
Identity Switching remains one of the main difficulties Multiple Object Tracking (MOT) algorithms have to deal with. Many state-of-the-art approaches now use sequence models to solve this problem but their training can be affected by biases that decrease their efficiency. In this paper, we introduce a new training procedure that confronts the algorithm to its own mistakes while explicitly attempting to minimize the number of switches, which results in better training. We propose an iterative scheme of building a rich training set and using it to learn a scoring function that is an explicit pro…
University of Science and Technology of China（中国科学技术大学）；The Chinese Academy of Sciences（中国科学院）
Multiple-object tracking and behavior analysis have been the essential parts of surveillance video analysis for public security and urban management. With billions of surveillance video captured all over the world, multiple-object tracking and behavior analysis by manual labor are cumbersome and cost expensive. Due to the rapid development of deep learning algorithms in recent years, automatic object tracking and behavior analysis put forward an urgent demand on a large scale well-annotated surveillance video dataset that can reflect the diverse, congested, and complicated scenarios in real ap…
Eskisehir Technical University（埃斯基谢希尔技术大学）；Eskisehir Osmangazi University（埃斯基谢希尔奥斯曼加齐大学）；Technical University of Munich（慕尼黑工业大学）
Robust object tracking requires knowledge of tracked objects' appearance, motion and their evolution over time. Although motion provides distinctive and complementary information especially for fast moving objects, most of the recent tracking architectures primarily focus on the objects' appearance information. In this paper, we propose a two-stream deep neural network tracker that uses both spatial and temporal features. Our architecture is developed over ATOM tracker and contains two backbones: (i) 2D-CNN network to capture appearance features and (ii) 3D-CNN network to capture motion featur…
With the guaranteed discrimination and efficiency of spatial appearance model, Discriminative Correlation Filters (DCF-) based tracking methods have achieved outstanding performance recently. However, the construction of effective temporal appearance model is still challenging on account of filter degeneration becomes a significant factor that causes tracking failures in the DCF framework. To encourage temporal continuity and to explore the smooth variation of target appearance, we propose to enhance low-rank structure of the learned filters, which can be realized by constraining the successiv…
End-to-End Self-Supervised RGB-T Tracking without Modality Misleading
China University of Mining and Technology（中国矿业大学）；Mine Digitization Engineering Research Center of the Ministry of Education（矿山数字化教育部工程研究中心）；University of Auckland（奥克兰大学）
This is a brief technical report of our proposed method for Multiple-Object Tracking (MOT) Challenge in Complex Environments. In this paper, we treat the MOT task as a two-stage task including human detection and trajectory matching. Specifically, we designed an improved human detector and associated most of detection to guarantee the integrity of the motion trajectory. We also propose a location-wise matching matrix to obtain more accurate trace matching. Without any model merging, our method achieves 66.672 HOTA and 93.971 MOTA on the DanceTrack challenge dataset.
Multiple human tracking is a fundamental problem for scene understanding. Although both accuracy and speed are required in real-world applications, recent tracking methods based on deep learning have focused on accuracy and require substantial running time. This study aims to improve running speed by performing human detection at a certain frame interval because it accounts for most of the running time. The question is how to maintain accuracy while skipping human detection. In this paper, we propose a method that complements the detection results with optical flow, based on the fact that some…
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation（弗劳恩霍夫光电、系统技术与图像利用研究所）
We present a method to perform online Multiple Object Tracking (MOT) of known object categories in monocular video data. Current Tracking-by-Detection MOT approaches build on top of 2D bounding box detections. In contrast, we exploit state-of-the-art instance aware semantic segmentation techniques to compute 2D shape representations of target objects in each frame. We predict position and shape of segmented instances in subsequent frames by exploiting optical flow cues. We define an affinity matrix between instances of subsequent frames which reflects locality and visual similarity. The instan…
In this paper we propose a system capable of tracking multiple soccer players in different types of video quality. The main goal, in contrast to most state-of-art soccer player tracking systems, is the ability of execute effectively tracking in videos of low-quality. We adapted a state-of-art Multiple Object Tracking to the task. In order to do that adaptation, we created a Detection and a Tracking Dataset for 3 different qualities of video. The results of our system are conclusive of its high performance.
Universitat Politècnica de Catalunya（加泰罗尼亚理工大学）；AutomaticTV
Multiple object tracking faces several challenges that may be alleviated with trajectory information. Knowing the posterior locations of an object helps disambiguating and solving situations such as occlusions, re-identification, and identity switching. In this work, we show that trajectory estimation can become a key factor for tracking, and present TrajE, a trajectory estimator based on recurrent mixture density networks, as a generic module that can be added to existing object trackers. To provide several trajectory hypotheses, our method uses beam search. Also, relying on the same estimate…
Multiple Object Tracking (MOT) plays an important role in solving many fundamental problems in video analysis in computer vision. Most MOT methods employ two steps: Object Detection and Data Association. The first step detects objects of interest in every frame of a video, and the second establishes correspondence between the detected objects in different frames to obtain their tracks. Object detection has made tremendous progress in the last few years due to deep learning. However, data association for tracking still relies on hand crafted constraints such as appearance, motion, spatial proxi…
FEMOT: Multi-Object Tracking using Frame and Event Cameras
School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；Peng Cheng Laboratory（鹏城实验室）；National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University（北京大学计算机学院多媒体信息处理全国重点实验室）；School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University（北京大学深圳研究生院电子与计算机工程学院）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））
QDTrack: Quasi-Dense Similarity Learning for Appearance-Only Multiple Object Tracking
ETH Zurich（苏黎世联邦理工学院）；Shanghai AI Laboratory（上海人工智能实验室）；Massachusetts Institute of Technology（麻省理工学院）；Stanford University（斯坦福大学）；UC Berkeley（加州大学伯克利分校）
提出类稠密相似性学习并构建 QDTrack，通过稠密区域对比学习和最近邻关联实现仅外观多目标跟踪，在 BDD100K MOT 上达到新最优且开销极小。
We present an online visual tracking algorithm by managing multiple target appearance models in a tree structure. The proposed algorithm employs Convolutional Neural Networks (CNNs) to represent target appearances, where multiple CNNs collaborate to estimate target states and determine the desirable paths for online model updates in the tree. By maintaining multiple CNNs in diverse branches of tree structure, it is convenient to deal with multi-modality in target appearances and preserve model reliability through smooth updates along tree paths. Since multiple CNNs share all parameters in conv…
University of Adelaide（阿德莱德大学）；ETH Zurich（苏黎世联邦理工学院）；Technische Universität Darmstadt（达姆施塔特工业大学）
Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide the most objective measure of performance and are therefore important guides for reseach. Recently, a new benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal of collecting existing and new data and creating a framework for the standardized evaluation of multiple object tracking methods. The first release of the benchmark focuses on multiple people tracking, since pedestrians are by far th…
This paper introduces a joint learning architecture (JLA) for multiple object tracking (MOT) and trajectory forecasting in which the goal is to predict objects' current and future trajectories simultaneously. Motion prediction is widely used in several state of the art MOT methods to refine predictions in the form of bounding boxes. Typically, a Kalman Filter provides short-term estimations to help trackers correctly predict objects' locations in the current frame. However, the Kalman Filter-based approaches cannot predict non-linear trajectories. We propose to jointly train a tracking and tra…
Concordia University（康考迪亚大学）；Chalmers University of Technology（查尔姆斯理工大学）；University of Arkansas（阿肯色大学）
This paper aims to tackle Multiple Object Tracking (MOT), an important problem in computer vision but remains challenging due to many practical issues, especially occlusions. Indeed, we propose a new real-time Depth Perspective-aware Multiple Object Tracking (DP-MOT) approach to tackle the occlusion problem in MOT. A simple yet efficient Subject-Ordered Depth Estimation (SODE) is first proposed to automatically order the depth positions of detected subjects in a 2D scene in an unsupervised manner. Using the output from SODE, a new Active pseudo-3D Kalman filter, a simple but effective extensio…
University of Ljubljana（卢布尔雅那大学）；Czech Technical University in Prague（布拉格捷克理工大学）
Template-based discriminative trackers are currently the dominant tracking paradigm due to their robustness, but are restricted to bounding box tracking and a limited range of transformation models, which reduces their localization accuracy. We propose a discriminative single-shot segmentation tracker -- D3S2, which narrows the gap between visual object tracking and video object segmentation. A single-shot network applies two target models with complementary geometric properties, one invariant to a broad range of transformations, including non-rigid deformations, the other assuming a rigid obj…
The recent trend in multiple object tracking (MOT) is heading towards leveraging deep learning to boost the tracking performance. In this paper, we propose a novel solution named TransSTAM, which leverages Transformer to effectively model both the appearance features of each object and the spatial-temporal relationships among objects. TransSTAM consists of two major parts: (1) The encoder utilizes the powerful self-attention mechanism of Transformer to learn discriminative features for each tracklet; (2) The decoder adopts the standard cross-attention mechanism to model the affinities between …
