Real-time people localization and tracking through fixed stereo vision

Bahadori, S.; Iocchi, Luca; Leone, Giuseppe Riccardo; Nardi, Daniele; Scozzafava, L.

doi:10.1007/s10489-006-0013-3

Cited by 42 publications

(57 citation statements)

References 30 publications

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“…There are some extensions of the occupancy map, for instance, Harville [16] proposed a plan-view height map and plan-view color map, which represent the height and color of the pixel having the largest depth value at a certain plan-view position. The combined use of depth and color allows efficient discrimination of people in close proximity to each other [15], [18], [19]. Moreover, the multi-view approach using occupancy maps was also exploited to enhance occlusion robustness, as in [2], [17].…”

Section: Tracking With Depthmentioning

confidence: 99%

Occlusion-Robust Human Tracking with Integrated Multi-View Depth Imagery

Fukushi

Kumazawa

2014

IEICE Trans. Inf. & Syst.

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Kenichiro FUKUSHI†a) and Itsuo KUMAZAWA † †b) , Members SUMMARYIn this paper, we present a computer vision-based human tracking system with multiple stereo cameras. Many widely used methods, such as KLT-tracker, update the trackers "frame-to-frame," so that features extracted from one frame are utilized to update their current state. In contrast, we propose a novel optimization technique for the "multi-frame" approach that computes resultant trajectories directly from video sequences, in order to achieve high-level robustness against severe occlusion, which is known to be a challenging problem in computer vision. We developed a heuristic optimization technique to estimate human trajectories, instead of using dynamic programming (DP) or an iterative approach, which makes our method sufficiently computationally efficient to operate in realtime. Six video sequences where one to six people walk in a narrow laboratory space are processed using our system. The results confirm that our system is capable of tracking cluttered scenes in which severe occlusion occurs and people are frequently in close proximity to each other. Moreover, minimal information is required for tracking, instead of full camera images, which is communicated over the network. Hence, commonly used network devices are sufficient for constructing our tracking system. key words: human tracking, multi-view, multi-frame, stereo vision, depth camera, occlusion robust IntroductionComputer vision-based human tracking has received increasing attention recently. Applications include ambient intelligence, human-computer interaction, human behavior analysis and security. Computer vision (CV) allows tracking systems that operate without such sensors as RFID, GPS, or smart phones. However, a problem known as "occlusion" in CV-based tracking exists, which occurs when a person being tracked goes behind other people or objects. Full occlusion disables any cues for tracking, and partial occlusion changes the appearance of the person, causing tracking difficulties.Earlier work has identified several promising strategies. First, multi-view tracking approaches are employed to reduce the blind areas caused by occlusion. Tracking or feature extraction is conducted for each camera, and then the final tracking result is produced by fusing the evidence from all of the views. The problem is how to match regions observed from different viewpoints. Researchers proposed

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Section: Tracking With Depthmentioning

confidence: 99%

Occlusion-Robust Human Tracking with Integrated Multi-View Depth Imagery

Fukushi

Kumazawa

2014

IEICE Trans. Inf. & Syst.

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“…Under these assumptions, this process corresponds to people localization and tracking process with a fixed stereo camera (PLT), as described in [3,10]. The PLT system has been thus used for the first phase, providing for segmenting the image by distinguishing the foreground person from the background, by background subtraction considering both intensity and disparity (see [3] for details).…”

Section: Image Segmentation and Appearance Model Acquisitionmentioning

confidence: 99%

Person Following through Appearance Models and Stereo Vision Using a Mobile Robot

Calisi

Iocchi²,

Leone

2007

Robot Vision

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Abstract. Following a person is an important task for mobile service and domestic robots in applications in which human-robot interaction is a primary requirement. In this paper we present an approach that integrates appearance models and stereo vision for efficient people tracking in domestic environments. Stereo vision helps in obtaining a very good segmentation of the scene to detect a person during the automatic model acquisition phase, and to determine the position of the target person in the environment. A navigation module and a high level person following behavior are responsible for performing the task in dynamic and cluttered environments. Experimental results are provided to demonstrate the effectiveness of the proposed approach.

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“…The stereo vision based People Localization and Tracking (PLT) [1,12] is composed by three processing modules: 1) segmentation based on background subtraction, that is used to detect foreground people to be tracked; 2) plan-view analysis, that is used to refine foreground segmentation and to compute observations for tracking; 3) tracking, that tracks observations over time maintaining association between tracks and tracked people (or objects).…”

Section: People Localization and Trackingmentioning

confidence: 99%

“…The stereo vision based people tracker in [1] has been used to provide XYZ-RGB data of tracked person in the scene. The tracker processes 640x480 images at about 10 frame per seconds, thus giving us high resolution and high rate data.…”

Section: Experimental Evaluationmentioning

confidence: 99%

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Human Posture Tracking and Classification Through Stereo Vision

Pellegrini

Iocchi²

2006

Proceedings of the First International Conference on Computer Vision Theory and Applications

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The ability of detecting human postures is very relevant for applications related to the analysis of human behaviors. Techniques for posture detection and classification can be thus very relevant in several fields, like ambient intelligence, surveillance, elderly care, human-machine interaction. This problem has been studied in recent years in the Computer Vision community, but proposed solutions still suffer from some limitations that are due to the difficulty of dealing with complex scenes (e.g., occlusions, different view points, etc.).In this article we present a system for posture tracking and classification based on a stereo vision sensor that provides both a robust way to segment and track people in the scene and 3D information about tracked people. The proposed method is based on matching 3D data with a 3D human body model. Relevant points in the model are then tracked over time with temporal filters and a classification method based on Hidden Markov Models is used to recognize principal postures. Experimental results show the effectiveness of the system in determining human postures with different orientations of the people with respect to the stereo sensor, in presence of partial occlusions and under different environmental conditions.

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Real-time people localization and tracking through fixed stereo vision

Cited by 42 publications

References 30 publications

Occlusion-Robust Human Tracking with Integrated Multi-View Depth Imagery

Occlusion-Robust Human Tracking with Integrated Multi-View Depth Imagery

Person Following through Appearance Models and Stereo Vision Using a Mobile Robot

Human Posture Tracking and Classification Through Stereo Vision

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