2015
DOI: 10.48550/arxiv.1504.01942
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MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking

Abstract: In the recent past, the computer vision community has developed centralized benchmarks for the performance evaluation of a variety of tasks, including generic object and pedestrian detection, 3D reconstruction, optical flow, single-object short-term tracking, and stereo estimation. Despite potential pitfalls of such benchmarks, they have proved to be extremely helpful to advance the state of the art in the respective area. Interestingly, there has been rather limited work on the standardization of quantitative… Show more

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Cited by 211 publications
(349 citation statements)
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“…Metrics MOT benchmarks [7,17,26] uses metrics from CLEAR [2], which includes Multiple-Object Tracking Accuracy (MOTA), Identity F1 score (IDF1), Identity Switch (IDsw), False Positive (FP), False Negative (FN) detections, as well as Mostly Tracked (MT) and Mostly Lost (ML) trajectories.…”
Section: Datasets and Metricsmentioning
confidence: 99%
“…Metrics MOT benchmarks [7,17,26] uses metrics from CLEAR [2], which includes Multiple-Object Tracking Accuracy (MOTA), Identity F1 score (IDF1), Identity Switch (IDsw), False Positive (FP), False Negative (FN) detections, as well as Mostly Tracked (MT) and Mostly Lost (ML) trajectories.…”
Section: Datasets and Metricsmentioning
confidence: 99%
“…Predicting trajectories in the MOT2D15 data set [28] We experiment using the MOT2D15 pedestrian video tracking dataset [28]. It is a public benchmark data set that contains short street videos with pedestrians, and for which the goal is to track each of them while they appear in the frame.…”
Section: Pedestrian Tracking With Partial Matching Informationmentioning
confidence: 99%
“…PETS [14] dataset is one of the earliest in this area. And the more recent MOT15 [21] dataset and the following MOT17 [27] and MOT20 [11] datasets are all popular in this community. These datasets are limited in some aspects we care about.…”
Section: Related Workmentioning
confidence: 99%