Sparse representation-based human detection: a scale-embedded dictionary approach

Abstract

Human detection is a complex problem owing to the variable pose that they can adopt. Here, we address this problem in sparse representation framework with an overcomplete scale-embedded dictionary. Histogram of oriented gradient features extracted from the candidate image patches are sparsely represented by the dictionary that contain positive bases along with negative and trivial bases. The object is detected based on the proposed likelihood measure obtained from the distribution of these sparse coefficients. The likelihood is obtained as the ratio of contribution of positive bases to negative and trivial bases. The positive bases of the dictionary represent the object (human) at various scales. This enables us to detect the object at any scale in one shot and avoids multiple scanning at different scales. This significantly reduces the computational complexity of detection task. In addition to human detection, it also finds the scale at which the human is detected due to the scale-embedded structure of the dictionary.

A hybrid LBP-DCNN based feature extraction method in YOLO: An application for masked face and social distance detection

“…Furthermore, multi-sensors were used for this task in [ 1 , 57 ]. The HOG feature extractor was used by the authors of [ 24 ]. The authors then used a dictionary to represent the extracted features, including positive, negative, and trivial bases.…”