MOT20Det Results

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

Benchmark Statistics

0.90 84.3 80.6 5.0 311,920 22,183 31,604 90.8 93.4 92.1 14.3
J. Hyun, M. Kang, D. Wee, D. Yeung. Detection recovery in online multi-object tracking with sparse graph tracker. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023.
0.88 38.3 79.1 41.4 317,139 185,547 26,385 92.3 63.1 75.0 29.4
M. Elbatel, H. Maher, M. Abouzeid, A. Bayoumi. Persolo: A Pedestrian Is a Person Through Thick and Thin. In , 2022.
0.81 79.3 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.
0.80 46.0 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.

Evaluation Measures

Lower is better. Higher is better.
Measure Better Perfect Description
AP higher 1Average 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 0The average number of false alarms per frame.
TP higher #GTThe total number of true positives.
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).
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
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.