Pedestrian Detection

This MATLAB package includes the implementation of the pedestrian detection algorithm.

Individualness and Determinantal Point Processes for Pedestrian Detection

  • Abstract: In this paper, we introduce individualness of detection candidates as a complement to objectness for pedestrian detection. The individualness assigns a single detection for each object out of raw detection candidates given by either object proposals or sliding windows. We show that conventional approaches, such as non-maximum suppression, are sub-optimal since they suppress nearby detections using only detection scores. We use a determinantal point process combined with the individualness to optimally select final detections. It models each detection using its quality and similarity to other detections based on the individualness. Then, detections with high detection scores and low correlations are selected by measuring their probability using a determinant of a matrix, which is composed of quality terms on the diagonal entries and similarities on the off-diagonal entries. For concreteness, we focus on the pedestrian detection problem as it is one of the most challenging problems due to frequent occlusions and unpredictable human motions. Experimental results demonstrate that the proposed algorithm works favorably against existing methods, including non-maximal suppression and a quadratic unconstrained binary optimization based method.
  • Bibtex entry: 
@inproceedings {lee:PedestrianDetection:eccv16,
  author    = {Donghoon Lee and Geonho Cha and Ming-Hsuan Yang and Songhwai Oh},
  title     = {Individualness and Determinantal Point Processes for Pedestrian Detection}, 
  bocktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
  month     = {October},
  year      = {2016}


An example is provided in DPPdetection.m.


This software is made available for free for non-commercial use. The software must not be modified or distributed without prior permission of the author. Please send your request to In your email, please include your name and institution. By submitting this request you agree to be bound by this license.

Current version: 0.1, October, 2016