3D-ZeF20 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	MOTA	IDF1	MOTP	MT	ML	FP	FN	Rcll	Prcn	FAF	ID Sw.	Frag	MTBFm	Hz
Naive 1.	51.0	63.0	34.4	2 (11.1)	0 (0.0)	1,370	6,552	59.6	87.6	0.4	18 (30.2)	520 (8.7)	9.0	1.8
Naive 1.	M. Pedersen, J. Haurum, S. Bengtson, T. Moeslund. 3D-ZeF: A 3D Zebrafish Tracking Benchmark Dataset. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020.

Sequences	Frames	Trajectories	Boxes
4	3600	18	32400

Evaluation Measures

Lower is better. Higher is better.

Measure	Better	Perfect	Description
MOTA	higher	100%	Multi-Object Tracking Accuracy (+/- denotes standard deviation across all sequences) [1]. This measure combines three error sources: false positives, missed targets and identity switches.
IDF1	higher	100%	ID F1 Score [2]. The ratio of correctly identified detections over the average number of ground-truth and computed detections.
MOTP	higher	100%	Multi-Object Tracking Precision (+/- denotes standard deviation across all sequences) [1]. The misalignment between the annotated and the predicted bounding boxes.
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).
Rcll	higher	100%	Ratio of correct detections to total number of GT boxes.
Prcn	higher	100%	Ratio of TP / (TP+FP).
FAF	lower	0	The average number of false alarms per frame.
ID Sw.	lower	0	Number of Identity Switches (ID switch ratio = #ID switches / recall) [3]. Please note that we follow the stricter definition of identity switches as described in the reference
Frag	lower	0	The total number of times a trajectory is fragmented (i.e. interrupted during tracking).
MTBFm	higher	#Frames	Monotonic Mean Time Between Failures [4]. The mean tracking duration with no errors (Identity switches, False Negatives, or Fragments)
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.
[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.
[4]	Carr, P. & Collins, R.T. Assessing tracking performance in complex scenarios using mean time between failures. In 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), 2016.