Efficient object-based surveillance image search using spatial pooling of convolutional features

Modern surveillance networks are large collections of computational sensor nodes, where each node can be programmed to capture, prioritize, segment salient objects, and transmit them to central repositories for indexing. Visual data from such networks grow exponentially and present many challenges concerning their transmission, storage, and retrieval. Searching for particular surveillance objects is a common but challenging task. In this paper, we present an efficient features extraction framework which utilizes an optimal subset of kernels from the first layer of a convolutional neural network pre-trained on ImageNet dataset for object-based surveillance image search. The input image is convolved with the set of kernels to generate feature maps, which are aggregated into a single feature map using a novel spatial maximal activator pooling approach. A low-dimensional feature vector is computed to represent surveillance objects. The proposed system provides improvements in both performance and efficiency over other similar approaches for surveillance datasets.