3D MOT 2015 Results

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

Showing only entries that use public detections!

TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
1. online method using public detections
0.028.7% 17.9% 2,0775,746380 (5.8)418 (6.4)0.1Public
T. Klinger, F. Rottensteiner, C. Heipke. Probabilistic Multi-Person Tracking using Dynamic Bayes Networks. In ISPRS Workshop on Image Sequence Analysis (ISA), 2015.
2. online method using public detections
0.025.7% 17.2% 1,8136,300311 (5.0)386 (6.2)0.1Public
T. Klinger, F. Rottensteiner, C. Heipke. Probabilistic multi-person localisation and tracking in image sequences. In ISPRS Journal of Photogrammetry and Remote Sensing, 2017.
3. using public detections new
0.025.7% 16.8% 3,0296,700363 (6.0)529 (8.8)17.7Public
N. Wojke, D. Paulus. Global data association for the Probability Hypothesis Density filter using network flows. In 2016 IEEE International Conference on Robotics and Automation, ICRA, 2016.
4. using public detections
0.013.8% 21.6% 2,0318,206520 (10.2)601 (11.8)8.4Public
L. Leal-Taixé, G. Pons-Moll, B. Rosenhahn. Everybody needs somebody: modeling social and grouping behavior on a linear programming multiple people tracker. In IEEE International Conference on Computer Vision Workshops (ICCVW). 1st Workshop on Modeling, Simulation and Visual Analysis of Large Crowds, 2011.
5. using public detections
0.020.9% 16.4% 3,5886,593580 (9.6)659 (10.9)83.5Public
MOT baseline: Linear programming on 3D image coordinates.
6. using public detections
0.011.2% 25.4% 3,0577,454532 (9.6)611 (11.0)1.9Public
Longyin Wen, Zhen Lei, Ming-Ching Chang, Honggang Qi, Siwei Lyu. Multi-Camera Multi-Target Tracking with Space-Time-View Hyper-graph. IJCV, 2016.
7. online method using public detections
0.03.0% 27.6% 2,0389,0841,462 (31.9)1,647 (35.9)1.2Public
A. Sadeghian, A. Alahi, S. Savarese. Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies. In ICCV, 2017.
8. online method using public detections
0.06.7% 14.6% 3,1617,5991,838 (33.6)1,686 (30.8)30.6Public
S. Pellegrini, A. Ess, K. Schindler, L. Gool. You'll never walk alone: Modeling social behavior for multi-target tracking.. In ICCV, 2009.

Benchmark Statistics


Difficulty Analysis

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



(40.8% MOTA)



(25.2% MOTA)

Evaluation Measures

Lower is better. Higher is better.
Measure Better Perfect Description
Avg Rank lower 1 This is the rank of each tracker averaged over all present evaluation measures.
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.


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.
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] 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.