2018/09/24 by Hiren Galiyawala, Galiyawala, Hiren, Kenil Shah +5 · 1 voice
Computer Science · Engineering · #Gait Recognition and Analysis #Human Pose and Action Recognition #Video Surveillance and Tracking Methods #cs.CV
paper · pdf · doi:10.48550/arxiv.1810.05080
openalex publication_date 2018/09/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A person is commonly described by attributes like height, build, cloth color, cloth type, and gender. Such attributes are known as soft biometrics. They bridge the semantic gap between human description and person retrieval in surveillance video. The paper proposes a deep learning-based linear filtering approach for person retrieval using height, cloth color, and gender. The proposed approach uses Mask R-CNN for pixel-wise person segmentation. It removes background clutter and provides precise boundary around the person. Color and gender models are fine-tuned using AlexNet and the algorithm is tested on SoftBioSearch dataset. It achieves good accuracy for person retrieval using the semantic query in challenging conditions.