CVPR 2019 Tracking Challenge Results

Download all results for this benchmark

Click on a measure to sort the table accordingly. See below for a more detailed description.


Showing only entries that use public detections!

Benchmark Statistics

TrackerMOTAIDF1HOTAMTMLFPFNRcllPrcnAssADetAAssReAssPrDetReDetPrLocAFAFID Sw.FragHz
SRK_ODESA
1. online method using public detections 		54.8 52.2 0.0 444 (35.4)241 (19.2)33,814 215,572 61.5 91.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7.5 3,750 (61.0)5,493 (89.3)1.2
D. Borysenko, D. Mykheievskyi, V. Porokhonskyy. ODESA: Object Descriptor that is Smooth Appearance-wise for object tracking tasks. In (to be submitted to ECCV'20), .
Tracktor++
2. online method using public detections 		51.3 47.6 0.0 313 (24.9)326 (26.0)16,263 253,680 54.7 95.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.6 2,584 (47.2)4,824 (88.2)2.7
P. Bergmann, T. Meinhardt, L. Leal-Taixé. Tracking without bells and whistles. In ICCV, 2019.
DD_TAMA19
3. online method using public detections 		47.6 48.7 0.0 342 (27.2)297 (23.6)38,194 252,934 54.8 88.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 8.5 2,437 (44.4)3,887 (70.9)0.2
Y. Yoon, D. Kim, Y. Song, K. Yoon, M. Jeon. Online Multiple Pedestrians Tracking using Deep Temporal Appearance Matching Association. In Information Sciences, 2020.
V_IOU
4. using public detections 		46.7 46.0 0.0 288 (22.9)306 (24.4)33,776 261,964 53.2 89.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7.5 2,589 (48.6)4,354 (81.8)18.2
E. Bochinski, T. Senst, T. Sikora. Extending IOU Based Multi-Object Tracking by Visual Information. In IEEE International Conference on Advanced Video and Signals-based Surveillance, 2018.
HAM_HI
5. online method using public detections 		43.0 43.6 0.0 353 (28.1)274 (21.8)72,018 243,055 56.6 81.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 16.1 4,153 (73.4)4,801 (84.8)0.8
Y. Yoon, A. Boragule, Y. Song, K. Yoon, M. Jeon. Online Multi-Object Tracking with Historical Appearance Matching and Scene Adaptive Detection Filtering. In IEEE AVSS, 2018.
IOU_19
6. using public detections 		35.8 25.7 0.0 126 (10.0)389 (31.0)24,427 319,696 42.9 90.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.5 15,676 (365.3)17,864 (416.3)183.3
E. Bochinski, V. Eiselein, T. Sikora. High-Speed Tracking-by-Detection Without Using Image Information. In International Workshop on Traffic and Street Surveillance for Safety and Security at IEEE AVSS 2017, 2017.
SequencesFramesTrajectoriesBoxes
444791492803370

Evaluation Measures

Lower is better. Higher is better.
Measure Better Perfect Description
MOTA higher 100%Multi-Object Tracking Accuracy (+/- denotes standard deviation across all sequences) [1]. This measure combines three error sources: false positives, missed targets and identity switches.
IDF1 higher 100%ID F1 Score [2]. The ratio of correctly identified detections over the average number of ground-truth and computed detections.
HOTA higher 100%Higher Order Tracking Accuracy [3]. Geometric mean of detection accuracy and association accuracy. Averaged across localization thresholds.
MT higher 100%Mostly tracked targets. The ratio of ground-truth trajectories that are covered by a track hypothesis for at least 80% of their respective life span.
ML lower 0%Mostly lost targets. The ratio of ground-truth trajectories that are covered by a track hypothesis for at most 20% of their respective life span.
FP lower 0The total number of false positives.
FN lower 0The total number of false negatives (missed targets).
Rcll higher 100%Ratio of correct detections to total number of GT boxes.
Prcn higher 100%Ratio of TP / (TP+FP).
AssA higher 100%Association Accuracy [3]. Association Jaccard index averaged over all matching detections and then averaged over localization thresholds.
DetA higher 100%Detection Accuracy [3]. Detection Jaccard index averaged over localization thresholds.
AssRe higher 100%Association Recall [3]. TPA / (TPA + FNA) averaged over all matching detections and then averaged over localization thresholds.
AssPr higher 100%Association Precision [3]. TPA / (TPA + FPA) averaged over all matching detections and then averaged over localization thresholds.
DetRe higher 100%Detection Recall [3]. TP /(TP + FN) averaged over localization thresholds.
DetPr higher 100%Detection Precision [3]. TP /(TP + FP) averaged over localization thresholds.
LocA higher 100%Localization Accuracy [3]. Average localization similarity averaged over all matching detections and averaged over localization thresholds.
FAF lower 0The average number of false alarms per frame.
ID Sw. lower 0Number of Identity Switches (ID switch ratio = #ID switches / recall) [4]. Please note that we follow the stricter definition of identity switches as described in the reference
Frag lower 0The total number of times a trajectory is fragmented (i.e. interrupted during tracking).
Hz higher Inf.Processing speed (in frames per second excluding the detector) on the benchmark. The frequency is provided by the authors and not officially evaluated by the MOTChallenge.

Legend

Symbol Description
online method This is an online (causal) method, i.e. the solution is immediately available with each incoming frame and cannot be changed at any later time.
using public detections This method used the provided detection set as input.
using private detections This method used a private detection set as input.
new This entry has been submitted or updated less than a week ago.

References:


[1] Bernardin, K. & Stiefelhagen, R. Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. Image and Video Processing, 2008(1):1-10, 2008.
[2] Ristani, E., Solera, F., Zou, R., Cucchiara, R. & Tomasi, C. Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking. In ECCV workshop on Benchmarking Multi-Target Tracking, 2016.
[3] Jonathon Luiten, A.O. & Leibe, B. HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking. International Journal of Computer Vision, 2020.
[4] Li, Y., Huang, C. & Nevatia, R. Learning to associate: HybridBoosted multi-target tracker for crowded scene. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2009.



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