MOT20Det Results

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



Benchmark Statistics

TrackerAPMODAMODPFAFTPFPFNRecallPrecisionF1Hz
Bresee_Det
1.
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
ViPeD20
2.
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.
SequencesFramesTrajectoriesBoxes
444791501765465

Difficulty Analysis

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

MOT20-07

MOT20-07

(0.89 AP)

MOT20-04

MOT20-04

(0.85 AP)

MOT20-08

MOT20-08

(0.75 AP)

MOT20-06

MOT20-06

(0.70 AP)


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).
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.

Legend

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.

References:


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