A Scalable Clustered Camera System for Multiple Object Tracking

Abstract

Reliable and efficient tracking of objects by multiple cameras is an important and challenging problem, which finds wide-ranging application areas. Most existing systems assume that data from multiple cameras is processed on a single processing unit or by a centralized server. However, these approaches are neither scalable nor fault tolerant. We propose multicamera algorithms that operate on peer-to-peer computing systems. Peer-to-peer vision systems require codesign of image processing and distributed computing algorithms as well as sophisticated communication protocols, which should be carefully designed and verified to avoid deadlocks and other problems. This paper introduces the scalable clustered camera system, which is a peer-to-peer multicamera system for multiple object tracking. Instead of transferring control of tracking jobs from one camera to another, each camera in the presented system performs its own tracking, keeping its own trajectories for each target object, which provides fault tolerance. A fast and robust tracking algorithm is proposed to perform tracking on each camera view, while maintaining consistent labeling. In addition, a novel communication protocol is introduced, which can handle the problems caused by communication delays and different processor loads and speeds, and incorporates variable synchronization capabilities, so as to allow flexibility with accuracy tradeoffs. This protocol was exhaustively verified by using the SPIN verification tool. The success of the proposed system is demonstrated on different scenarios captured by multiple cameras placed in different setups. Also, simulation and verification results for the protocol are presented.

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