Visual sensor networks are one potential enabler for the evolution of the Internet of things. Due to their limited resources in terms of energy and bandwidth, it is crucial to identify appropriate approaches that take into considerations such constraints and reduce the amount of data transmitted to the gathering point (sink). In this context, this paper describes the impact of a distributed smart-camera system that exploits an analyze-then-compress strategy, on a multi-view vehicle tracking at roundabouts application. In the tested system, part of the processing is shifted to the smart cameras, i.e., the object detection/classification and feature extraction, so that only the extracted features describing moving vehicles are transmitted instead of the whole image/video. Features are further compacted by using a state-of-the-art distributed coding technique, based upon an efficient clustering method that exploits the temporal and spatial (multiple views) correlations between features. The system is tested on a real-data scenario, by evaluating the bit-rate reduction capabilities in dependence of the channel conditions, as well as the matching accuracy of the reconstructed descriptors in the specific tracking application. Both feature-wise and object-wise matching are investigated. For the chosen application scenario, a bit-rate reduction of 30 − 35% is proved to be achievable in non-ideal channel conditions. Even more interestingly, such reduction is proved not to harm the matching accuracy (i.e., it is coherent with the target application), for which an F-score up to 0.923 is guaranteed.
This paper presents a solution for low power consumption in digital systems designs. The Double Base Number System (DBNS) representation recognized by its high sparseness is used for digital filters coefficients representation to reduce the switching activity, a key factor in power consumption.In this paper, we show that the use of the Canonic DBNS representation for filter coefficients representation leads to a dynamic power reduction average of 30% compared to the 2's complement representation. The second contribution of our work consists in the application of sub-expression sharing on the filter coefficients, already expressed in DBNS representation. Strengths and limitations of this optimization will be discussed in details throughout this paper.
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