MOT20Det Results

Download all results for this benchmark

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

Benchmark Statistics

Tracker	AP	MODA	MODP	FAF	TP	FP	FN	Rcll	Prcn	F1	Hz
SW_YoloX_det 1.	0.90	85.8	82.4	4.6	315,227	20,493	28,297	91.8	93.9	92.8	17.0
SW_YoloX_det 1.	C.-Y. Tsai, R.-Y. Wang and Y.-C. Chiu, “SW-YOLOX: A Real-Time Pedestrian Detection System Based on Sliding Window-Mixed Attention Mechanism in YOLOX Lightweight Network,” Neurocomputing, accepted for publication, DOI: https://doi.org/10.1016/j.neucom.2024.128357
SGT_det 2.	0.90	84.3	80.6	5.0	311,920	22,183	31,604	90.8	93.4	92.1	14.3
SGT_det 2.	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.
Persolo_V2 3.	0.88	38.3	79.1	41.4	317,139	185,547	26,385	92.3	63.1	75.0	29.4
Persolo_V2 3.	M. Elbatel, H. Maher, M. Abouzeid, A. Bayoumi. Persolo: A Pedestrian Is a Person Through Thick and Thin. In , 2022.
GNN_SDT 4.	0.81	79.3	80.0	7.1	304,236	31,677	39,288	88.6	90.6	89.6	1.2
GNN_SDT 4.	Y. Wang, X. Weng, K. Kitani. Joint Detection and Multi-Object Tracking with Graph Neural Networks. In arXiv, 2020.
ViPeD20 5.	0.80	46.0	79.0	31.1	297,101	139,111	46,277	86.5	68.1	76.2	11.2
ViPeD20 5.	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

Evaluation Measures

Lower is better. Higher is better.

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).
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.

Legend

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.

References:

[1]	Bernardin, K. & Stiefelhagen, R. Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. Image and Video Processing, 2008(1):1-10, 2008.