A Fault-Tolerant Distributed Vision System Architecture for Object Tracking in a Smart Room

Abstract

Abstract. In recent years, distributed computer vision has gained a lot of attention within the computer vision community for applications such as video surveillance and object tracking. The collective information gathered by multiple cameras that are strategically placed has many advantages. For example, aggregation of information from multiple viewpoints reduces the uncertainty about the scene. Further, there is no single point of failure, thus the system as a whole could continue to perform the task at hand. However, the advantages arising out of such cooperation can be realized only by timely sharing of the information between them. This paper discusses the design of a distributed vision system that enables several heterogeneous sensors with different processing rates to exchange information in a timely manner in order to achieve a common goal, say tracking of multiple human subjects and mobile robots in an indoor smart environment. In our fault-tolerant distributed vision system, a resource manager manages individual cameras and buffers the time-stamped object candidates received from them. A User Agent with a given task specification approaches the resource manager, first for knowing the available resources (cameras) and later for receiving the object candidates from the resources of its interest. Thus the resource manager acts as a proxy between the user agents and cameras, thereby freeing the cameras to do dedicated feature detection and extraction only. In such a scenario, many failures are possible. For example, one of the cameras may have a hardware failure or it may lose the target, which moved away from its field of view. In this context, important issues such as failure detection and handling, synchronization of data from multiple sensors and sensor reconfiguration by view planning are discussed in the paper. Experimental results with real scene images will be given.

Real-time cooperative multi-target tracking by communicating active vision agents

“…With a pan-tilt camera, it is difficult to detect arbitrary objects from an observed image. In [14], a moving object is detected by simply analyzing flows estimated in consecutive images. The system described in [5] can detect moving objects by subtracting consecutive images even if the camera is rotated during the observation.…”

“…Thus, it is impossible to solve this problem with previous tracking systems, especially in cases in which a tracking system possesses a large number of active cameras. In [14], for example, pan-tilt-zoom cameras were employed for tracking target objects. The system used, however, at most two (non-active) panoramic cameras and two pan-tilt-zoom cameras.…”

Centralized and Distributed Multi-view Correspondence

“…(Karuppiah et al, 2001;Collins et al, 2000;Narayanan, 1995;Brumitt et al, 2000). Common to all these tasks is the need to aggregate information across multiple sensors.…”

Integrated System for Stereoscopic Cognitive Vision, Localization, Mapping, and Communication with a Mobile Service Robot

“…In the adaptive stereo model, the sensor geometry can be controlled to manage the precision of the resulting virtual stereo system [14]. Other indoor and outdoor stereo vision systems have been developed and tested with satisfactory results and some drawbacks, see [15], [16] and [17].…”