Multi-Object Tracking
116 papers with code • 11 benchmarks • 18 datasets
Multiple Object Tracking is the problem of automatically identifying multiple objects in a video and representing them as a set of trajectories with high accuracy.
Benchmarks
These leaderboards are used to track progress in Multi-Object Tracking
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Libraries
Use these libraries to find Multi-Object Tracking models and implementations
MOT17
MOTChallenge
BDD100K
MOT16
Virtual KITTI
MOT15
UAVDT
Virtual KITTI 2
TAO
MOT20
Subtasks
Most implemented papers
Simple Online and Realtime Tracking
abewley/sort
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This paper explores a pragmatic approach to multiple object tracking where the main focus is to associate objects efficiently for online and realtime applications.
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
ifzhang/FairMOT
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Formulating MOT as multi-task learning of object detection and re-ID in a single network is appealing since it allows joint optimization of the two tasks and enjoys high computation efficiency.
Towards Real-Time Multi-Object Tracking
Zhongdao/Towards-Realtime-MOT
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ECCV 2020
In this paper, we propose an MOT system that allows target detection and appearance embedding to be learned in a shared model.
Tracking without bells and whistles
phil-bergmann/tracking_wo_bnw
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ICCV 2019
Therefore, we motivate our approach as a new tracking paradigm and point out promising future research directions.
MOT16: A Benchmark for Multi-Object Tracking
PaddlePaddle/PaddleDetection
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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.
ByteTrack: Multi-Object Tracking by Associating Every Detection Box
ifzhang/ByteTrack
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arXiv 2021
ByteTrack also achieves state-of-the-art performance on MOT20, HiEve and BDD100K tracking benchmarks.
Tracking Objects as Points
xingyizhou/CenterTrack
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ECCV 2020
Nowadays, tracking is dominated by pipelines that perform object detection followed by temporal association, also known as tracking-by-detection.
Rethinking the competition between detection and ReID in Multi-Object Tracking
JudasDie/SOTS
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However, the inherent differences and relations between detection and re-identification (ReID) are unconsciously overlooked because of treating them as two isolated tasks in the one-shot tracking paradigm.
TraSw: Tracklet-Switch Adversarial Attacks against Multi-Object Tracking
To our knowledge, this is the first work on the adversarial attack against pedestrian MOT trackers.
Improving Object Detection, Multi-object Tracking, and Re-Identification for Disaster Response Drones
In the second approach, although DeepSORT only processes a quarter of all frames due to hardware and time limitations, our model with DeepSORT (42. 9%) outperforms FairMOT (71. 4%) in terms of recall.
