DETRAC: A New Benchmark and Protocol for Multi-Object Tracking

By Longyin Wen, Dawei Du, Zhaowei Cai, Zhen Lei, Ming-Ching Chang, Honggang Qi, Jongwoo Lim, Ming-Hsuan Yang, Siwei Lyu. 2015

63 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming (2019, background): “…conditions [Sakaridis et al., 2018a,b, Volk et al., 2019, Bernuth et al., 2019, Hospach et al., 2016, Bernuth et al., 2018] or collecting real [Wen et al., 2015, Yu et al., 2018, Che et al., 2019, Caesar et al., 2019] and artiﬁcial [Gaidon et al., 2016, Ros et al., 2016, Richter et al.,…”
MOT16: A Benchmark for Multi-Object Tracking (2016, neutral): “Inthenear future, DETRAC, a new benchmark for vehicle tracking [44], is going to open a similar submission system to the one we proposed with MOTChallenge .”
Context-Aware Single-Shot Detector (2017, methodology): “Our experimental results on VOC [5], MS-COCO [17] and DETRAC [31] show that both DiCSSD and DeCSSD outperform SSD while maintaining real-time speed, in addition to producing promising detection results on small objects.”
The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking (2018, methodology): “To evaluate these algorithms fairly, the computer vision community has developed plenty of datasets including detection datasets ( e.g. , Caltech [14] and DETRAC [46]) and tracking datasets ( e.g. , KITTI-T [19] and VOT2016 [15]).”
End-to-end Recurrent Multi-Object Tracking and Trajectory Prediction with Relational Reasoning (2019, methodology): “The MOTChallenge and the UA-DETRAC dataset discussed in this section are intended to be used as a benchmark suite for multi-object-tracking in a tracking-by-detection paradigm.”