Target detection and counting using a progressive certainty map in distributed visual sensor networks

Visual sensor networks (VSNs) merge computer vision, image processing and wireless sensor network disciplines to solve problems in multi-camera applications by providing valuable information through distributed sensing and collaborative in-network processing. Collaboration in sensor networks is necessary not only to compensate for the processing, sensing, energy, and bandwidth limitations of each sensor node but also to improve the accuracy and robustness of the sensor network. Collaborative processing in VSNs is more challenging than in conventional scalar sensor networks (SSNs) because of two unique features of cameras, including the extremely higher data rate compared to that of scalar sensors and the directional sensing characteristics with limited field of view. In this paper, we study a challenging computer vision problem, target detection and counting in VSN environment. Traditionally, the problem is solved by counting the number of intersections of the backprojected 2D cones of each target. However, the existence of visual occlusion among targets would generate many false alarms. In this work, instead of resolving the uncertainty about target existence at the intersections, we identify and study the non-occupied areas in the cone and generate the so-called certainty map of non-existence of targets. This way, after fusing inputs from a set of sensor nodes, the unresolved regions on the certainty map would be the location of target. This paper focuses on the design of a light-weight, energy-efficient, and robust solution where not only each camera node transmits a very limited amount of data but that a limited number of camera nodes is used. We propose a dynamic itinerary for certainty map integration where the entire map is progressively clarified from sensor to sensor. When the confidence of the certainty map is satisfied, a geometric counting algorithm is applied to find the estimated number of targets. In the conducted experiments using real data, the results of the proposed distributed and progressive method shows effectiveness in detection accuracy and energy and bandwidth efficiency.