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Extraction of discriminative features is crucial in person re-identification (ReID) which aims to match a query image of a person to her/his images, captured by different cameras. The conventional deep feature extraction methods on ReID employ CNNs with static convolutional kernels, where the kernel parameters are optimized during the training and remain constant in the inference. This approach limits the network's ability to model complex contents and decreases performance, particularly when dealing with occlusions or pose changes. In this work, to improve the performance without a significant increase in parameter size, we present a novel approach by utilizing a channel fusion-based dynamic convolution backbone network, which enables the kernels to change adaptively based on the input image, within two existing ReID network architectures. We replace the backbone network of two ReID methods to investigate the effect of dynamic convolution on both simple and complex networks. The first one called Baseline, is a simpler network with fewer layers, while the second, CaceNet represents a more complex architecture with higher performance. Evaluation results demonstrate that both of the designed dynamic networks improve identification accuracy compared to the static counterparts. A significant increase in accuracy is reported under occlusion tested on Occluded-DukeMTMC. Moreover, our approach achieves a performance comparable to the state-of-the-art on Market1501, DukeMTMC-reID, and CUHK03 with a limited computational load. These findings validate the effectiveness of the dynamic convolution in enhancing the person ReID networks and push the boundaries of performance in this domain. [ABSTRACT FROM AUTHOR]

In this paper, we propose a proximity-based indoor positioning system which is capable of monitoring mobile device user's indoor locations where the commonly used GPS signal is unavailable or weak. The designed system is aimed to be integrated into a context-aware communications system to prevent transmission of irrelevant content to all users but easing delivery of the location-based information. Similar to the beacon technology that assigns a code to each targeted position in an indoor location, our system labels the locations with audio watermark codes where user's mobile device monitors and receives the watermarked audio. The proposed encoder performs code-division multiplexing that allows insertion of several location indexes into the same audio file. Watermark embedded through spread spectrum improves robustness to noise and guarantees a satisfactory performance even though the mobile device has a low band microphone. The designed decoder installs synchronization between the mobile device and the watermarked audio emitter in real time, and extracts the embedded watermark code words assigned to specific indoor locations. This invokes the context-aware content delivery module and the delivery is initiated. Position displacements of the mobile users are estimated by the time-of-flight technique and the users moving within the coverage range of the emitters are continuously monitored. Decoding is achieved in real time that enables the mobile users to reach to content delivered from different emitters within their coverage range. Performance tests demonstrate that the developed system enables to estimate the user position within the 7-m distance from the emitter while keeping inaudibility. We reached 2-m spatial resolution in discrimination of different emitters. The proposed framework can be considered as a promising alternative to latest technologies, i.e., Wi-Fi-based fingerprinting systems or beacons. [ABSTRACT FROM AUTHOR]