MOT20 Results

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

TrackerMOTAIDF1MOTPMTMLFPFNRecallPrecisionFAFID Sw. FragHz
HTBT
1. using public detections
48.9
±21.9
54.6
±10.4
78.0 384 (30.9)274 (22.1)45,660 216,803 58.1 86.8 10.2 2,187 (37.6)3,067 (52.8)0.2
Anonymous submission
track_bnw
2. online method using public detections
50.8
±21.3
52.1
±15.5
76.8 472 (38.0)240 (19.3)58,689 193,199 62.7 84.7 13.1 2,751 (43.9)4,233 (67.6)0.2
Anonymous submission
TBDX
3. using public detections
43.9
±22.5
31.4
±9.7
76.1 312 (25.1)284 (22.9)50,710 235,059 54.6 84.8 11.3 4,311 (79.0)5,754 (105.4)16.9
Anonymous submission
BBT
4. using public detections
46.8
±21.3
42.2
±12.0
78.0 312 (25.1)289 (23.3)35,014 236,176 54.4 88.9 7.8 3,880 (71.4)7,207 (132.6)8.0
Anonymous submission
SFS
5. online method using public detections
50.8
±17.4
41.1
±9.6
74.9 341 (27.5)251 (20.2)50,139 200,932 61.2 86.3 11.2 3,503 (57.3)7,617 (124.5)0.1
Anonymous submission
SORT20
6. online method using public detections
42.7
±18.6
45.1
±13.1
78.5 208 (16.7)326 (26.2)27,521 264,694 48.8 90.2 6.1 4,470 (91.5)17,798 (364.4)57.3
A. Bewley, Z. Ge, L. Ott, F. Ramos, B. Upcroft. Simple online and realtime tracking. In 2016 IEEE International Conference on Image Processing (ICIP), 2016.
SequencesFramesTrajectoriesBoxes
444791501765465

Difficulty Analysis

Sequence difficulty (from easiest to hardest, measured by average MOTA)

MOT20-04

MOT20-04

(65.9 MOTA)

MOT20-07

MOT20-07

(52.9 MOTA)

MOT20-06

MOT20-06

(26.8 MOTA)

MOT20-08

MOT20-08

(14.3 MOTA)


Evaluation Measures

Lower is better. Higher is better.
Measure Better Perfect Description
MOTA higher 100 % Multiple Object Tracking Accuracy [1]. This measure combines three error sources: false positives, missed targets and identity switches.
MOTP higher 100 % Multiple Object Tracking Precision [1]. The misalignment between the annotated and the predicted bounding boxes.
IDF1 higher 100 % ID F1 Score [2]. The ratio of correctly identified detections over the average number of ground-truth and computed detections.
FAF lower 0 The average number of false alarms per frame.
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 0 The total number of false positives.
FN lower 0 The total number of false negatives (missed targets).
ID Sw. lower 0 The total number of identity switches. Please note that we follow the stricter definition of identity switches as described in [3].
Frag lower 0 The 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 public detections This method used the provided detection set as input.
new This entry has been submitted or updated less than a week ago.
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] 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.