Deep Residual Network Video Summarization for Face Detection and Person Re-Identification

The surge in the adoption of video-based applications can be attributed to the growing accessibility of video data. Videos, in particular, serve as extensive, multimodal records of events, enabling profound investigative insights and the discernment of behavioral patterns in individuals. Within various applications, the precision of models is significantly enhanced through the utilization of video sequences, owing to their ability to provide precise temporal signals and rich appearance information. One specific domain, known as person re-identification (Re-ID), focuses on the precise location of individuals within query images or videos using data extracted from non-overlapping Closed-Circuit Television (CCTV) sources. Person re-identification assumes pivotal significance in domains such as criminal investigation and human tracking, notably within the realm of intelligent video surveillance systems. Our experimentation was conducted using the PyTorch framework, employing the ‘EPFL’ dataset, which consists of Multi-camera Pedestrian Videos. To underpin our investigations, deep learning technologies, including recurrent neural networks, convolutional neural networks, and generative adversarial networks, have been systematically harnessed. These contemporary video-based re-identification techniques have showcased exceptional performance, facilitated by the ongoing advancements in deep learning technology.