Joint multi-person detection and tracking from overlapping cameras

Liem, Martijn; Gavrila, Dariu M.

doi:10.1016/j.cviu.2014.06.003

Cited by 35 publications

(16 citation statements)

References 36 publications

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“…What differentiates the learning-based approaches is the learning metrics (classifier) that are used to identify or re-identify a person. Some of the metrics or classifiers that are normally been utilized are the shape model [128], boosting method [129] [138], nearest neighbor approach [109] and least square reduction [139].…”

Section: ) Learning-based Re-idmentioning

confidence: 99%

Literature Survey on Multi-Camera System and Its Application

2020

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A multi-camera system combines features from different cameras to exploit a scene of an event to increase the output image quality. The combination of two or more cameras requires prior settings in terms of calibration and architecture. Therefore, this paper surveys the available literature in terms of multi-camera systems' physical arrangements, calibrations, algorithms, and their advantages and disadvantages. We also survey the recent developments and advancements in four areas of multi-camera system applications, which are surveillance, sports, education, and mobile phones. In the surveillance system, the combination of multiple heterogeneous cameras and the discovery of Pan-Tilt-Zoom (PTZ) and smart cameras have brought tremendous achievements in the area of multi-camera control and coordination. Different approaches have been proposed to facilitate effective collaboration and monitoring among the camera network. Furthermore, the application of multi-cameras in sports has made the games more interesting in the aspect of analyses and transparency. The application of the multi-camera system in education has taken education beyond the four walls of the class. The method of teaching, student attendance enrollment, determination of students' attention, teacher and student assessment can now be determined with ease, and all forms of proxy and manipulation in education can be reduced by using a multi-camera system. Besides, the number of cameras featuring on smartphones is gaining noticeable recognition. However, most of these cameras serve different purposes, from zooming, telephoto, and wider Field of View (FOV). Therefore, future smartphones should be expecting more cameras or the development would be in a different direction.

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Section: ) Learning-based Re-idmentioning

confidence: 99%

Literature Survey on Multi-Camera System and Its Application

2020

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“…Liem & Gavrila proposed a multi-people position tracking algorithm for overlapping cameras [37]. The similarity of a person was mapped to the running track using a set of hints.…”

Section: Literature Surveymentioning

confidence: 99%

An Inexpensive Upgradation of Legacy Cameras Using Software and Hardware Architecture for Monitoring and Tracking of Live Threats

Habiba

Awais

Jaleel³

2020

IEEE Access

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Surveillance through digital cameras is increasing exponentially. A majority of these cameras are not smart cameras; therefore, they send their video stream to a central server where it is processed and analyzed for any threats. Typically, human operators or machine learning algorithms at the cloud analyzed and processed the post-event videos to track and locate the perpetrator or victim. The centralized approach leads to two primary shortcomings: 1) the high cost of cloud infrastructure; 2) lack of instant tracking and detection of the threat. One solution is to replace these legacy cameras with the smart cameras so they can process information locally. Although the solution is costly, it could solve the real-time threat detection issues. However, the need for a central server remains there, to construct the path of threat, when threat moves from one camera view to another. The existing distributed architectures for threat tracking shifts the load of threat capturing and processing from a central server to the edge nodes, which in turn reduces the computational power but does not remove the role of the central server completely. These architectures don't equip each camera of processing and communicating with each other. Further, in the existing distributed architectures, the local cameras are not able to store the path of the threat individually and just transmit the captured trajectory to the central body. This research proposed a second alternative that makes use of legacy cameras through additional hardware and software components such that they can process information and collaborate locally. The research addresses the challenge by introducing a low cost distributed threat tracking framework that allows the single camera to identify the threat and communicate its information to other cameras without involving the central server. The framework stores the information in a lightweight architecture that is inspired by the blockchain storage algorithm. The system also allows querying the path traveled by the threat at any stage. To evaluate the system, we performed two simulated experiments: one with a central server and another with the proposed distributed system. The results of the experiments showed that the time to track the threat through the proposed system was lower than the existing centralized system. Moreover, the proposed system predicted the paths of threats with an accuracy of 85.49%. In the future, the technique may be improved with reinforcement learning and other machine learning techniques. INDEX TERMS Video surveillance, smart cameras, distributed computing, software architecture, peer-topeer computing.

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“…Consequently, object tracking via multiple cameras has been widely examined by the researchers in this field, who have adopted different approaches to this innovation. A method for tracking the position of multiple people in a scene by using overlapping cameras has been reported in literature [7]. In the study, a two-step approach that would simultaneously track people and estimate their position is proposed.…”

Section: Related Workmentioning

confidence: 99%

Convolutional Gating Network for Object Tracking

Feizi

2019

IJE

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A B S T R A C TObject tracking through multiple cameras is a popular research topic in security and surveillance systems especially when human objects are the target. However, occlusion is one of the challenging problems for the tracking process. T his paper proposes a multiple-camera-based cooperative tracking method to overcome the occlusion problem. The paper presents a new model for combining convolutional neural networks (CNNs), which allows the proposed method to learn the features with high discriminative power and geometrical independence. In the training phase, the CNNs are first pre-trained in each of the camera views, and a convolutional gating network (CGN) is simultaneously pre-trained to produce a weight for each CNN output. The CNNs are then transferred to the tracking task where the pre-trained parameters of the CNNs are re-trained by using the data from the tracking phase. T he weights obtained from the CGN are used in order to fuse the features learnt by the CNNs and the resulting weighted combination of the features is employed to represent the objects. Finally, the p article filter is used in order to track objects. The experimental results showed the efficiency of the proposed method in this paper.

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Joint multi-person detection and tracking from overlapping cameras

Cited by 35 publications

References 36 publications

Literature Survey on Multi-Camera System and Its Application

Literature Survey on Multi-Camera System and Its Application

An Inexpensive Upgradation of Legacy Cameras Using Software and Hardware Architecture for Monitoring and Tracking of Live Threats

Convolutional Gating Network for Object Tracking

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