Tracking persons with multiple cameras with overlapping fields of view instead of with one camera leads to more robust decisions. However, operating multiple cameras instead of one requires more processing power and communication bandwidth, which are limited resources in practical networks. When the fields of view of different cameras overlap, not all cameras are equally needed for localizing a tracking target. When only a selected set of cameras do processing and transmit data to track the target, a substantial saving of resources is achieved. The recent introduction of smart cameras with on-board image processing and communication hardware makes such a distributed implementation of tracking feasible. We present a novel framework for selecting cameras to track people in a distributed smart camera network that is based on generalized information-theory. By quantifying the contribution of one or more cameras to the tracking task, the limited network resources can be allocated appropriately, such that the best possible tracking performance is achieved. With the proposed method, we dynamically assign a subset of all available cameras to each target and track it in difficult circumstances of occlusions and limited fields of view with the same accuracy as when using all cameras.

Pulse-code modulation (PCM) with embedded quantization allows the rate of the PCM bitstream to be reduced by simply removing a fixed number of least significant bits from each codeword. Although this source coding technique is extremely simple, it has a poor coding efficiency. In this paper, we present a generalized PCM (GPCM) algorithm for images that simply removes bits from each codeword. In contrast to PCM, however, the number and the specific bits that a GPCM encoder removes in each codeword depends on its position in the bitstream and the statistics of the image. Since GPCM allows the encoding to be performed with different degrees of computational complexity, it can adapt to the computational resources that are available in each application. Experimental results show that GPCM outperforms PCM with a gain that depends on the rate, the computational complexity of the encoding, and the degree of inter-pixel correlation of the image.

Within a camera network, the contribution of a camera to the observation of a scene depends on its viewpoint and on the scene configuration. This is a dynamic property, as the scene content is subject to change over time. An automatic selection of a subset of cameras that significantly contributes to the desired observation of a scene can be of great value for the reduction of the amount of transmitted or stored image data. In this work, we propose low data rate schemes to select from a vision network a subset of cameras that provides a good frontal observation of the persons in the scene and allows for the best approximation of their 3D shape. We also investigate to what degree low data rates trade off quality of reconstructed 3D shapes.

In some video coding applications, it is desirable to reduce the complexity of the video encoder at the expense of a more complex decoder. Distributed video (DV) coding is a new paradigm that aims to achieve this. To allocate a proper number of bits to each frame, most DV coding algorithms use a feedback channel (FBC). However, in some cases, a FBC does not exist. In this paper, we therefore propose a rate allocation (RA) algorithm for pixel-domain distributed video coders without FBC. Our algorithm estimates at the encoder the number of bits for every frame without significantly increasing the encoder complexity. Experimental results show that our RA algorithm delivers satisfactory estimates of the adequate encoding rate, especially for sequences with little motion.