GMOT-40: A Benchmark for Generic Multiple Object Tracking

Bai, Hexin; Cheng, Wensheng; Chu, Peng; Liu, Juehuan; Zhang, Kai; Ling, Haibin

doi:10.1109/cvpr46437.2021.00665

Cited by 26 publications

(24 citation statements)

References 41 publications

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“…Apollo MOTS [111], TAO-person [230], WildTrack [231], and GMOT-40 [232]. Details of these datasets can be found in the reference.…”

Section: Datasetsmentioning

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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“…Apollo MOTS [111], TAO-person [230], WildTrack [231], and GMOT-40 [232]. Details of these datasets can be found in the reference.…”

Section: Datasetsmentioning

confidence: 99%

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

Wang¹,

Song²,

Hwang³

2022

Preprint

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“…GMOT-40. GMOT-40 [1] is a recently proposed benchmark that aims at one-shot MOT. It consists of 40 sequences from 10 categories.…”

Section: Mots Motsmentioning

confidence: 99%

“…Different from the above datasets for MOT on pedestrians, vehicles or other subjects, AnimalTrack focuses on dense multi-animal tracking in the wild. Although some of the benchmarks (e.g., TAO [15] and GMOT-40 [1]) contain animal targets for tracking, they have limitations for MAT. For TAO [15], the average trajectory is 6 and even lower for animal, the average trajectory is 4.…”

Section: Mots Motsmentioning

confidence: 99%

“…As a platform, it allows researchers to develop their algorithms and fairly assess, compare and analyze different approaches for improvement. Currently, there exist many datasets [1,15,18,19,22,36,61] for MOT on different subjects in various scenarios. Nevertheless, there is no available benchmark dedicated for multiple animal tracking.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, there is no available benchmark dedicated for multiple animal tracking. Although some of the datasets (e.g., [1,15]) consist of video sequences involved with animal targets, they are limited in either video quantity and animal categories [1] or number of animal tracklets [15], which makes them not an ideal platform for studying MAT. In order to facilitate MOT on animals, a dedicated benchmark is urgently required for both designing and evaluating MAT algorithms.…”

Section: Introductionmentioning

confidence: 99%

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AnimalTrack: A Large-scale Benchmark for Multi-Animal Tracking in the Wild

Zhang¹,

Gao²,

Zhang³

et al. 2022

Preprint

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Multi-animal tracking (MAT), a multi-object tracking (MOT) problem, is crucial for animal motion and behavior analysis and has many crucial applications such as biology, ecology, animal conservation and so forth. Despite its importance, MAT is largely underexplored compared to other MOT problems such as multi-human tracking due to the scarcity of large-scale benchmark. To address this problem, we introduce An-imalTrack, a large-scale benchmark for multi-animal tracking in the wild. Specifically, AnimalTrack consists of 58 sequences from a diverse selection of 10 common animal categories. On average, each sequence comprises of 33 target objects for tracking. In order to ensure high quality, every frame in AnimalTrack is manually labeled with careful inspection and refinement. To our best knowledge, AnimalTrack is the first benchmark dedicated to multi-animal tracking. In addition, to understand how existing MOT algorithms perform on An-imalTrack and provide baselines for future comparison, we extensively evaluate 14 state-of-the-art representa-* Libo Zhang and Junyuan Gao are co-first authors.

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