AVSS Challenges 2018 Soft Biometric Retrieval Using Deep Multi-Task Network

摘要

In surveillance, humans are the agents performing actions to change the states in the scene. They are the main focus in the surveillance systems and, therefore, the design of processing methods focusing on humans is extremely important to identify a person and determine his/her role in the scene. Thus, one of the goals of smart surveillance systems is to address the automatic video understanding by applying computer vision techniques to automatically detect specific humans in video streams based on attributes, such as soft biometrics. For that purpose, this work proposes an approach that receives a set of textual attributes as a query and searches for people by matching those attributes in a gallery of images, as defined in the challenge Semantic Person Retrieval in Surveillance Using Soft Biometrics Challenge, proposed in AVSS 2018. We address this problem with a multitask learning approach hypothesizing that the attributes available for the query are highly related to each other that could be learned together in the same network as different tasks. Finally, we performed both qualitative and quantitative experimental evaluations, indicating a promising direction for the proposed approach.