Pedestrian detection

Despite the challenges, pedestrian detection still remains an active research area in computer vision in recent years.

Implementation of this approach follows a standard procedure for processing the image data that consists of first creating a densely sampled image pyramid, computing features at each scale, performing classification at all possible locations, and finally performing non-maximal suppression to generate the final set of bounding boxes.

[5] In 2005, Leibe et al.[6] proposed an approach combining both the detection and segmentation with the name Implicit Shape Model (ISM).

This procedure highlights the silhouettes (the connected components in the foreground) of every moving element in the scene, including people.

In this approach, The ground plane is partitioned into uniform, non-overlapping grid cells, typically with size of 25 by 25 (cm).

Given two to four synchronized video streams taken at eye level and from different angles, this method can effectively combine a generative model with dynamic programming to accurately follow up to six individuals across thousands of frames in spite of significant occlusions and lighting changes.