Convolutional neural networks (CNN) allow achieving the highest accuracy for the task of object detection in images. Major challenges in further development of object detectors are false-positive detections and high demand of processing power. In this paper, we propose an approach to object detection which makes it possible to reduce the number of false-positive detections by processing only moving objects and reduce the required processing power for algorithm inference. The proposed approach is a modification of CNN already trained for object detection task. This method can be used to improve the accuracy of an existing system by applying minor changes to the algorithm. The efficiency of the proposed approach was demonstrated on the open dataset "CDNet2014 pedestrian". The implementation of the method proposed in the article is available on the GitHub: https://github.com/david-svitov/AmphibianDetector
The network trained for domain adaptation is prone to bias toward the easy-to-transfer classes. Since the ground truth label on the target domain is unavailable during training, the bias problem leads to skewed predictions, forgetting to predict hard-to-transfer classes. To address this problem, we propose Cross-domain Moving Object Mixing (CMOM) that cuts several objects, including hard-to-transfer classes, in the source domain video clip and pastes them into the target domain video clip. Unlike image-level domain adaptation, the temporal context should be maintained to mix moving objects in …
Adaptive Detection of Fast Moving Celestial Objects Using a Mixture of Experts and Physical-Inspired Neural Network
College of Physics and Optoelectronics, Taiyuan University of Technology（太原理工大学物理与光电工程学院）；Key Laboratory of Space Object and Debris Observation, Purple Mountain Observatory, Chinese Academy of Sciences（中国科学院紫金山天文台空间目标与碎片观测重点实验室）
Shandong University（山东大学）；The School of Control Science and Engineering (CSE) of Shandong University（山东大学控制科学与工程学院）
Dynamic target detection and target tracking are hot issues in the field of image. In order to explore its application value in the field of mobile robot, a dynamic target detection and tracking system is designed based on hexapod robot. Firstly, the dynamic target detection method is introduced with region merging and adaptive external point filtering based on motion compensation method. This method achieves the accurate compensation of the moving background through symmetric matching and adaptive external point filtering, and achieves complete detection of non-rigid objects by region merging…
JSTR: Joint Spatio-Temporal Reasoning for Event-based Moving Object Detection
National Key Lab of Multispectral Information Intelligent Processing Technology（多光谱信息智能处理技术国家重点实验室）；School of Artificial Intelligence and Automation（人工智能与自动化学院）；Huazhong University of Science and Technology（华中科技大学）
Moving object detection and its associated background-foreground separation have been widely used in a lot of applications, including computer vision, transportation and surveillance. Due to the presence of the static background, a video can be naturally decomposed into a low-rank background and a sparse foreground. Many regularization techniques, such as matrix nuclear norm, have been imposed on the background. In the meanwhile, sparsity or smoothness based regularizations, such as total variation and $\ell_1$, can be imposed on the foreground. Moreover, graph Laplacians are further imposed t…
Video representation is an important and challenging task in the computer vision community. In this paper, we assume that image frames of a moving scene can be modeled as a Linear Dynamical System. We propose a sparse coding framework, named adaptive video dictionary learning (AVDL), to model a video adaptively. The developed framework is able to capture the dynamics of a moving scene by exploring both sparse properties and the temporal correlations of consecutive video frames. The proposed method is compared with state of the art video processing methods on several benchmark data sequences, w…
This paper presents a novel method of foreground segmentation that distinguishes moving objects from their moving cast shadows in monocular image sequences. The models of background, edge information, and shadow are set up and adaptively updated. A Bayesian belief network is proposed to describe the relationships among the segmentation label, background, intensity, and edge information. The notion of Markov random field is used to encourage the spatial connectivity of the segmented regions. The solution is obtained by maximizing the posterior possibility density of the segmentation field.
Moving Light Adaptive Colonoscopy Reconstruction via Illumination-Attenuation-Aware 3D Gaussian Splatting
Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology（武汉光电国家研究中心，华中科技大学）；School of Computer Science, Wuhan University（武汉大学计算机学院）；Wuhan United Imaging Healthcare Surgical Technology Co., Ltd（武汉联影医疗手术技术有限公司）
Self-supervised deep learning-based 3D scene understanding methods can overcome the difficulty of acquiring the densely labeled ground-truth and have made a lot of advances. However, occlusions and moving objects are still some of the major limitations. In this paper, we explore the learnable occlusion aware optical flow guided self-supervised depth and camera pose estimation by an adaptive cross weighted loss to address the above limitations. Firstly, we explore to train the learnable occlusion mask fused optical flow network by an occlusion-aware photometric loss with the temporally suppleme…
Real-Time Object Tracking with On-Device Deep Learning for Adaptive Beamforming in Dynamic Acoustic Environments
Department of Electronic Technology, Universidad Carlos III de Madrid, Spain（电子技术系，卡洛斯三世大学，西班牙）；Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Spain（信号理论与通信系，卡洛斯三世大学，西班牙）；Department of Computer Science, Universidad de Valencia, Spain（计算机科学系，瓦伦西亚大学，西班牙）
