Click on a measure to sort the table accordingly. See below for a more detailed description.
Tracker | AP | MODA | MODP | FAF | TP | FP | FN | Recall | Precision | F1 | Hz |
DeDeT 1. |
0.89 ±0.06 |
70.7 ±18.7 | 81.5 | 16.7 | 317,832 | 75,004 | 25,692 | 92.5 | 80.9 | 86.3 | 16.9 |
Bresee_Det 2. |
0.87 ±0.09 |
35.6 ±45.8 | 73.3 | 42.6 | 313,100 | 190,776 | 30,424 | 91.1 | 62.1 | 73.9 | 11.2 |
GNN_SDT 3. |
0.81 ±0.09 |
79.3 ±15.1 | 80.0 | 7.1 | 304,236 | 31,677 | 39,288 | 88.6 | 90.6 | 89.6 | 1.2 |
Y. Wang, X. Weng, K. Kitani. Joint Detection and Multi-Object Tracking with Graph Neural Networks. In arXiv, 2020. | |||||||||||
ViPeD20 4. |
0.80 ±0.07 |
46.0 ±40.5 | 79.0 | 31.1 | 297,101 | 139,111 | 46,277 | 86.5 | 68.1 | 76.2 | 11.2 |
L. Ciampi, N. Messina, F. Falchi, C. Gennaro, G. Amato. Virtual to Real Adaptation of Pedestrian Detectors. In Sensors, 2020. |
Sequences | Frames | Trajectories | Boxes |
4 | 4479 | 1501 | 765465 |
Sequence difficulty (from easiest to hardest, measured by average AP)
Measure | Better | Perfect | Description |
AP | higher | 1 | Average Precision taken over a set of reference recall values (0:0.1:1) |
MODA | higher | 100% | Multi-Object Detection Accuracy [1]. This measure combines false positives and missed targets. |
MODP | higher | 100% | Multi-Object Detection Precision [1]. The misalignment between the annotated and the predicted bounding boxes. |
FAF | lower | 0 | The average number of false alarms per frame. |
TP | higher | #GT | The total number of true positives. |
FP | lower | 0 | The total number of false positives. |
FN | lower | 0 | The total number of false negatives (missed targets). |
Recall | higher | 100% | Ratio of correct detections to total number of GT boxes. |
Precision | higher | 100% | Ratio of TP / (TP+FP). |
F1 | higher | 100% | Harmonic mean of precision and recall. |
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 |
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. | |
This method used the provided detection set as input. | |
This method used a private detection set as input. | |
![]() |
This entry has been submitted or updated less than a week ago. |
[1] | Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. Image and Video Processing, 2008(1):1-10, 2008. |