Clothing Classification using Corner Features in Pedestrian Attribute Recognition Framework

Syahmi Syahiran Bin Ahmad Ridzuan, Zaid Omar*, Usman Ullah Sheikh, Uswah Khairuddin, Anwar P.P. Abdul Majeed

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review


The recognition of pedestrian attributes has become increasingly important in ensuring community safety. It replaces the outdated and cumbersome method of identifying criminal characteristics with a more advanced, efficient, and accurate framework. With the widespread use of Closed-Circuit Television (CCTV) and the emergence of Big Data, an advanced analytic tool can now dissect and understand massive collections of video footage for multiple purposes. To identify pedestrians, this paper focuses on upper-body and lower-body clothing classification using the P-DESTRE dataset which provides multiple attributes for pedestrians. Prior to feature extraction, pre-processing steps using DeepLab for background removal and AlphaPose for body parts recognition are performed. The framework then classifies the collar, upper-body clothing, and lower-body clothing type by utilising a combination of Features from Accelerated Segment Test (FAST), FAST with Non-Maximal Suppression (FASTNMS), and Shi-Tomasi corner detectors. The findings indicate a classification rate of over 90% for all three elements, demonstrating the effectiveness of the method and establishing a framework for recognizing a pedestrian based on upper and lower body clothing.

Original languageEnglish
Title of host publicationWSSE 2023 - 2023 5th World Symposium on Software Engineering
PublisherAssociation for Computing Machinery
Number of pages7
ISBN (Electronic)9798400708053
Publication statusPublished - 22 Sept 2023
Event5th World Symposium on Software Engineering, WSSE 2023 - Tokyo, Japan
Duration: 22 Sept 202324 Sept 2023

Publication series

NameACM International Conference Proceeding Series


Conference5th World Symposium on Software Engineering, WSSE 2023


  • Computer Vision
  • Information Retrieval
  • Machine Learning


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