Local Metrics for Multi-Object Tracking

Valmadre, Jack; Bewley, Alex; Huang, Jonathan; Sun, Chen; Sminchisescu, Cristian; Schmid, Cordelia

doi:10.48550/arxiv.2104.02631

Cited by 5 publications

(9 citation statements)

References 62 publications

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“…[235] proposes the SAIDF evaluation measure that focuses more on identity issues and fixes the insensibility and high computational cost problems of the previous measures, such as MOTA and IDF1. Furthermore, [236] introduces local metrics, namely LIDF1 and ALTA, that are parametrized by a temporal horizon and thereby reveal the temporal ranges at which association errors occur. Such metrics provide more insights from different aspects for MOT evaluation.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Recent Advances in Embedding Methods for Multi-Object Tracking: A Survey

Wang¹,

Song²,

Hwang³

2022

Preprint

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Multi-object tracking (MOT) aims to associate target objects across video frames in order to obtain entire moving trajectories. With the advancement of deep neural networks and the increasing demand for intelligent video analysis, MOT has gained significantly increased interest in the computer vision community. Embedding methods play an essential role in object location estimation and temporal identity association in MOT. Unlike other computer vision tasks, such as image classification, object detection, re-identification, and segmentation, embedding methods in MOT have large variations, and they have never been systematically analyzed and summarized. In this survey, we first conduct a comprehensive overview with in-depth analysis for embedding methods in MOT from seven different perspectives, including patch-level embedding, single-frame embedding, cross-frame joint embedding, correlation embedding, sequential embedding, tracklet embedding, and cross-track relational embedding. We further summarize the existing widely used MOT datasets and analyze the advantages of existing state-of-the-art methods according to their embedding strategies. Finally, some critical yet under-investigated areas and future research directions are discussed.

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Section: Evaluation Metricsmentioning

confidence: 99%

Recent Advances in Embedding Methods for Multi-Object Tracking: A Survey

Wang¹,

Song²,

Hwang³

2022

Preprint

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“…The Multiple Object Tracking Accuracy (MOTA) combines IDS, FN and FP into a single score [1]. However, MOTA has a few limitations [30,17,22]: it is dependent on the video frame rate; it is unbounded and can be negative; and its expected behavior is not symmetric in terms of Recall and Precision. The IDF1 measure operates at sequencelevel by combining Precision and Recall of trajectories [27].…”

Section: Visual Tracking Performancementioning

confidence: 99%

“…private detections) are allowed in the MOT benchmark, exhibiting higher performance as compared to using public detections. Therefore, it would desirable to be able to compare different detectortracker combinations, as current MOT performance measures are unable to address such comparison fairly [30,17].…”

Section: Introductionmentioning

confidence: 99%

Detection-aware multi-object tracking evaluation

SanMiguel¹,

Munoz²,

Poiesi

2022

2022 18th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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How would you fairly evaluate two multi-object tracking algorithms (i.e. trackers), each one employing a different object detector? Detectors keep improving, thus trackers can make less effort to estimate object states over time. Is it then fair to compare a new tracker employing a new detector with another tracker using an old detector? In this paper, we propose a novel performance measure, named Tracking Effort Measure (TEM), to evaluate trackers that use different detectors. TEM estimates the improvement that the tracker does with respect to its input data (i.e. detections) at frame level (intra-frame complexity) and sequence level (inter-frame complexity). We evaluate TEM over well-known datasets, four trackers and eight detection sets. Results show that, unlike conventional tracking evaluation measures, TEM can quantify the effort done by the tracker with a reduced correlation on the input detections. Its implementation will be made publicly available online. 1 .

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“…For association-type tasks (MOT, MOTS and PoseTrack), we first report the MOTA metric for its popularity and because it has been shown to highly correlate with qualitative perception of tracking accuracy [4]. However, the MOTA metric disproportionately overweights detection accuracy [45], [66] over identity preservation within tracks, which for many applications is vital. For this reason, we also report identity based metrics such as IDF-1 and ID-switch.…”

Section: Associationmentioning

confidence: 99%

Do Different Tracking Tasks Require Different Appearance Models?

Wang¹,

Zhao²,

Li³

et al. 2021

Preprint

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Tracking objects of interest in a video is one of the most popular and widely applicable problems in computer vision. However, with the years, a Cambrian explosion of use cases and benchmarks has fragmented the problem in a multitude of different experimental setups. As a consequence, the literature has fragmented too, and now the novel approaches proposed by the community are usually specialised to fit only one specific setup. To understand to what extent this specialisation is actually necessary, in this work we present UniTrack, a solution to address five different tasks within the same framework. UniTrack consists of a single and task-agnostic appearance model, which can be learned in a supervised or self-supervised fashion, and multiple "heads" to address individual tasks and that do not require training. We show how most tracking tasks can be solved within this framework, and that the same appearance model can be used to obtain performance that is competitive against specialised methods for all the five tasks considered. The framework also allows us to analyse appearance models obtained with the most recent self-supervised methods, thus significantly extending their evaluation and comparison to a larger variety of important problems. Code available at this URL.

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Local Metrics for Multi-Object Tracking

Cited by 5 publications

References 62 publications

Recent Advances in Embedding Methods for Multi-Object Tracking: A Survey

Recent Advances in Embedding Methods for Multi-Object Tracking: A Survey

Detection-aware multi-object tracking evaluation

Do Different Tracking Tasks Require Different Appearance Models?

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