Paper Info
Title
AVSS Challenges 2018 Soft Biometric Retrieval Using Deep Multi-Task Network
Abstract
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.
Year
DOI
Venue
2018
10.1109/AVSS.2018.8639325
2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
Keywords
Field
DocType
Task analysis,Legged locomotion,Torso,Image color analysis,Surveillance,Training,Cameras
Computer vision,Soft biometrics,Multi-task learning,Task analysis,Computer science,Human–computer interaction,Artificial intelligence,Biometrics,Smart surveillance
Conference
ISBN
Citations 
PageRank 
978-1-5386-9294-3
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Gabriel Resende Gonçalves100.68
Antonio C. Nazare271.81
Matheus Alves Diniz331.38
Luiz Eduardo Coelho Lima400.34
William Robson Schwartz595675.15