Abstract Multiple Object Tracking (MOT) has gained increasing attention due to its academic
and commercial potential. Although different approaches have been proposed to tackle this …

The goal of this paper is to review the state-of-the-art progress on visual tracking methods,
classify them into different categories, as well as identify future trends. Visual tracking is a …

摘要随着摄像头安装数量的日益增多, 以及智慧城市和公共安全需求的日益增长,
采用人工的视频监控方式已经远远不能满足需要, 因此智能视频监控技术应运而生并迅速成为 …

Multi-object tracking (MOT) has been notoriously difficult to evaluate. Previous metrics
overemphasize the importance of either detection or association. To address this, we …

Standardized benchmarks are crucial for the majority of computer vision applications.
Although leaderboards and ranking tables should not be over-claimed, benchmarks often …

Due to balanced accuracy and speed, one-shot models which jointly learn detection and
identification embeddings, have drawn great attention in multi-object tracking (MOT) …

Tracking by detection, the dominant approach for online multi-object tracking, alternates
between localization and association steps. As a result, it strongly depends on the quality of …

Recent works have shown that convolutional networks have substantially improved the
performance of multiple object tracking by simultaneously learning detection and …

Standardized benchmarks have been crucial in pushing the performance of computer vision
algorithms, especially since the advent of deep learning. Although leaderboards should not …

Urban traffic optimization using traffic cameras as sensors is driving the need to advance
state-of-the-art multi-target multi-camera (MTMC) tracking. This work introduces CityFlow, a …