The 1st-place Solution for ECCV 2022 Multiple People Tracking in Group Dance Challenge

The Second-place Solution for ECCV 2022 Multiple People Tracking in Group Dance Challenge

This is our 2nd-place solution for the ECCV 2022 Multiple People Tracking in Group Dance Challenge. Our method mainly includes two steps: online short-term tracking using our Cascaded Buffer-IoU (C-BIoU) Tracker, and, offline long-term tracking using appearance feature and hierarchical clustering. Our C-BIoU tracker adds buffers to expand the matching space of detections and tracks, which mitigates the effect of irregular motions in two aspects: one is to directly match identical but non-overlapping detections and tracks in adjacent frames, and the other is to compensate for the motion estimat…

MOTRv2：利用预训练目标检测器引导端到端多目标跟踪

MOTRv2: Bootstrapping End-to-End Multi-Object Tracking by Pretrained Object Detectors

本文提出 MOTRv2，通过引入预训练目标检测器生成锚点候选框为 MOTR 提供检测先验，缓解检测与关联任务冲突，在 DanceTrack 和 BDD100K 上取得领先性能。

MeMoSORT：用于多人跟踪的记忆辅助滤波与运动自适应关联度量

MeMoSORT: Memory-Assisted Filtering and Motion-Adaptive Association Metric for Multi-Person Tracking

针对多人跟踪中复杂运动与遮挡导致的滤波误差和身份切换问题，提出具有记忆辅助卡尔曼滤波和运动自适应IoU的MeMoSORT跟踪器，在DanceTrack和SportsMOT上实现SOTA性能。

MEEV: Body Mesh Estimation On Egocentric Video

This technical report introduces our solution, MEEV, proposed to the EgoBody Challenge at ECCV 2022. Captured from head-mounted devices, the dataset consists of human body shape and motion of interacting people. The EgoBody dataset has challenges such as occluded body or blurry image. In order to overcome the challenges, MEEV is designed to exploit multiscale features for rich spatial information. Besides, to overcome the limited size of dataset, the model is pre-trained with the dataset aggregated 2D and 3D pose estimation datasets. Achieving 82.30 for MPJPE and 92.93 for MPVPE, MEEV has won …

FairFace Challenge at ECCV 2020: Analyzing Bias in Face Recognition

This work summarizes the 2020 ChaLearn Looking at People Fair Face Recognition and Analysis Challenge and provides a description of the top-winning solutions and analysis of the results. The aim of the challenge was to evaluate accuracy and bias in gender and skin colour of submitted algorithms on the task of 1:1 face verification in the presence of other confounding attributes. Participants were evaluated using an in-the-wild dataset based on reannotated IJB-C, further enriched by 12.5K new images and additional labels. The dataset is not balanced, which simulates a real world scenario where …

ChaLearn Looking at People: Inpainting and Denoising challenges

Dealing with incomplete information is a well studied problem in the context of machine learning and computational intelligence. However, in the context of computer vision, the problem has only been studied in specific scenarios (e.g., certain types of occlusions in specific types of images), although it is common to have incomplete information in visual data. This chapter describes the design of an academic competition focusing on inpainting of images and video sequences that was part of the competition program of WCCI2018 and had a satellite event collocated with ECCV2018. The ChaLearn Looki…