2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
Login Paper Search My Schedule Paper Index Help

My ICASSP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDMMSP-5.3
Paper Title A Triplet Appearance Parsing Network for Person Re-Identification
Authors Mingfu Xiong, Engineering Research Center of Hubei Province for Clothing Information, School of Mathematics and Computer Science, Wuhan Textile University, China; Zhongyuan Wang, School of Computer Science, Wuhan University, Wuhan 430072, China, China; Ruhan He, Xinrong Hu, School of Mathematics and Computer Science, Wuhan Textile University, China; Ming Cheng, Engineering Research Center of Hubei Province for Clothing Information, School of Mathematics and Computer Science, Wuhan Textile University, China; Xiao Qin, Shelby Center for Engineering Technology, Samuel Ginn College of Engineering, American Samoa; Jia Chen, School of Mathematics and Computer Science, Wuhan Textile University, China
SessionMMSP-5: Human Centric Multimedia 1
LocationGather.Town
Session Time:Thursday, 10 June, 14:00 - 14:45
Presentation Time:Thursday, 10 June, 14:00 - 14:45
Presentation Poster
Topic Multimedia Signal Processing: Signal Processing for Multimedia Applications
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract As one of the specific vision tasks, person re-identification has become a prevalent research topic in the field of multimedia and computer vision. However, existing feature extraction methods, originating from the quality of the bounding boxes which could cause the inhomogeneity and incoherence of person representation for cluttered backgrounds, are difficult to adapt the challenges of the harsh real-world scenarios. This study develops a Triplet person Appearances Parsing Framework (TAPF) which eliminates the surrounding interference factors of bounding boxes for person re-identification. The framework consists of a triplet person parsing network and an integration mechanism for person local and global appearance information. Concretely, the triplet parsing network includes a channel parsing module, a position parsing module and a color parsing module, which are used to extract the person channel parsing descriptor, regional descriptor and color perception descriptor, respectively. Then, a local and global flatten gaussian operations are performed to integrate the person appearance parsing descriptors to obtain more discriminative features for the person representation. The experimental results have been conducted to validate our proposed algorithm can achieve a better performance for person re-identification on several public datasets, i.e., VIPeR and Market-1501, respectively.