Feature Encoding in Band-limited Distributed Surveillance Systems
This work addresses bandwidth constraints in distributed wireless smart camera networks for security applications, representing an incremental improvement in feature encoding methods.
The paper tackles the challenge of transmitting high-dimensional data in bandwidth-limited distributed surveillance systems by proposing a novel probabilistic algorithm that reduces feature dimensionality to save network bandwidth, demonstrating effectiveness through experiments on two surveillance recognition tasks.
Distributed surveillance systems have become popular in recent years due to security concerns. However, transmitting high dimensional data in bandwidth-limited distributed systems becomes a major challenge. In this paper, we address this issue by proposing a novel probabilistic algorithm based on the divergence between the probability distributions of the visual features in order to reduce their dimensionality and thus save the network bandwidth in distributed wireless smart camera networks. We demonstrate the effectiveness of the proposed approach through extensive experiments on two surveillance recognition tasks.