CTMC-v1 Results

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

Benchmark Statistics

1. online method
51.0 58.5 55.16 481 (37.5)133 (10.4)190,267 273,567 71.2 78.0 2.6 2,010 (0.0)12,469 (0.0)18.5
2. online method
50.6 56.7 52.51 428 (33.3)174 (13.6)158,592 309,014 67.5 80.2 2.2 2,118 (0.0)10,453 (0.0)38.5
50.6 56.7 52.51 428 (33.3)174 (13.6)158,593 309,015 67.5 80.2 2.2 2,118 (0.0)10,453 (0.0)4.8
49.6 53.4 52.83 438 (34.1)166 (12.9)172,538 303,212 68.1 78.9 2.4 2,630 (0.0)8,219 (0.0)20.0
39.0 41.0 36.95 219 (17.1)317 (24.7)109,689 466,667 50.9 81.5 1.5 3,142 (0.0)20,829 (0.0)25.8
6. online method
35.1 50.5 51.95 427 (33.3)126 (9.8)299,941 312,368 67.1 68.0 4.2 4,318 (0.0)25,992 (0.0)48,072.7

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.
TRA higher 100%Tracking Accuracy [3]. Tracking accuracy for evaluating cells in videos
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).
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.


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.


[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] Ulman, V., ska, M.M., Magnusson, .E.G., Ronneberger, O., Haubold, C., Harder, N., Matula, P., Matula, P., Svoboda, D., Radojevic, M., Smal, I., Rohr, K., Jaldén, J., Blau, .M., Dzyubachyk, O. & Lelieveldt, B. An objective comparison of cell-tracking algorithms. Nature Methods (online), 14(12):1141-1152, Nature Publishing Group, 2017.
[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.