Multiple people tracking with articulation detection and stitching strategy

Liu, Yuanpei; Yin, Junbo; Yu, Dajiang; Zhao, Sanyuan; Shen, Jianbing

doi:10.1016/j.neucom.2019.12.037

Cited by 20 publications

(3 citation statements)

References 15 publications

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“…Using only the pose may not be robust enough, especially in crowded environments where trajectories are close and often intercept. Adding visual features for data association have been demonstrated to be more robust [20]. Also, Deep Learning has been explored for tracking [21].…”

Section: People Trackingmentioning

confidence: 99%

People Tracking in Panoramic Video for Guiding Robots

Bacchin¹,

Berno²,

Menegatti³

et al. 2022

Preprint

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A guiding robot aims to effectively bring people to and from specific places within environments that are possibly unknown to them. During this operation the robot should be able to detect and track the accompanied person, trying never to lose sight of her/him. A solution to minimize this event is to use an omnidirectional camera: its 360 • Field of View (FoV) guarantees that any framed object cannot leave the FoV if not occluded or very far from the sensor. However, the acquired panoramic videos introduce new challenges in perception tasks such as people detection and tracking, including the large size of the images to be processed, the distortion effects introduced by the cylindrical projection and the periodic nature of panoramic images. In this paper, we propose a set of targeted methods that allow to effectively adapt to panoramic videos a standard people detection and tracking pipeline originally designed for perspective cameras. Our methods have been implemented and tested inside a deep learning-based people detection and tracking framework with a commercial 360 • camera. Experiments performed on datasets specifically acquired for guiding robot applications and on a real service robot show the effectiveness of the proposed approach over other state-of-the-art systems. We release with this paper the acquired and annotated datasets and the open-source implementation of our method.

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Section: People Trackingmentioning

confidence: 99%

People Tracking in Panoramic Video for Guiding Robots

Bacchin¹,

Berno²,

Menegatti³

et al. 2022

Preprint

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show abstract

“…Trajectory Management: For trajectory initialization, we adopt method in [35] to alleviate the influence caused by FP detections. Besides, a target will be terminated if it moves out of the view or keeps occluded for over certain frames.…”

Section: Our Online Mot Pipelinementioning

confidence: 99%

A Unified Object Motion and Affinity Model for Online Multi-Object Tracking

Yin

Yang

Meng

et al. 2020

Preprint

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Current popular online multi-object tracking (MOT) solutions apply single object trackers (SOTs) to capture object motions, while often requiring an extra affinity network to associate objects, especially for the occluded ones. This brings extra computational overhead due to repetitive feature extraction for SOT and affinity computation. Meanwhile, the model size of the sophisticated affinity network is usually non-trivial. In this paper, we propose a novel MOT framework that unifies object motion and affinity model into a single network, named UMA, in order to learn a compact feature that is discriminative for both object motion and affinity measure. In particular, UMA integrates single object tracking and metric learning into a unified triplet network by means of multi-task learning. Such design brings advantages of improved computation efficiency, low memory requirement and simplified training procedure. In addition, we equip our model with a task-specific attention module, which is used to boost task-aware feature learning. The proposed UMA can be easily trained end-to-end, and is elegant -requiring only one training stage. Experimental results show that it achieves promising performance on several MOT Challenge benchmarks.

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“…In existing methods, some researchers exploit mobile phones [ 14 ] and wearable devices [ 15 ] to track the movement of pedestrians. Nowadays, the usual way is to extract the image features of pedestrians with convolutional neural networks (CNNs) [ 16 , 17 , 18 ]. Monocular pedestrian tracking for each camera is conducted first, and then image features of detected bounding boxes in consecutive frames are extracted; finally, these features are compared with those from other cameras to conduct the pedestrian matching, which is called pedestrian ReID.…”

Section: Introductionmentioning

confidence: 99%

Multi-Camera Multi-Person Tracking and Re-Identification in an Operating Room

Hachiuma

Saitō

et al. 2022

J. Imaging

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Multi-camera multi-person (MCMP) tracking and re-identification (ReID) are essential tasks in safety, pedestrian analysis, and so on; however, most research focuses on outdoor scenarios because they are much more complicated to deal with occlusions and misidentification in a crowded room with obstacles. Moreover, it is challenging to complete the two tasks in one framework. We present a trajectory-based method, integrating tracking and ReID tasks. First, the poses of all surgical members captured by each camera are detected frame-by-frame; then, the detected poses are exploited to track the trajectories of all members for each camera; finally, these trajectories of different cameras are clustered to re-identify the members in the operating room across all cameras. Compared to other MCMP tracking and ReID methods, the proposed one mainly exploits trajectories, taking texture features that are less distinguishable in the operating room scenario as auxiliary cues. We also integrate temporal information during ReID, which is more reliable than the state-of-the-art framework where ReID is conducted frame-by-frame. In addition, our framework requires no training before deployment in new scenarios. We also created an annotated MCMP dataset with actual operating room videos. Our experiments prove the effectiveness of the proposed trajectory-based ReID algorithm. The proposed framework achieves 85.44% accuracy in the ReID task, outperforming the state-of-the-art framework in our operating room dataset.

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Multiple people tracking with articulation detection and stitching strategy

Cited by 20 publications

References 15 publications

People Tracking in Panoramic Video for Guiding Robots

People Tracking in Panoramic Video for Guiding Robots

A Unified Object Motion and Affinity Model for Online Multi-Object Tracking

Multi-Camera Multi-Person Tracking and Re-Identification in an Operating Room

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