Pedestrian detection

Pedestrian detection is an essential and significant task in any intelligent video surveillance system, as it provides the fundamental information for semantic understanding of the video footages. It has an obvious extension to automotive applications due to the potential for improving safety systems. Many car manufacturers (e.g. Volvo, Ford, GM, Nissan) offer this as an ADAS option in 2017. Various style of clothing in appearance Different possible articulations The presence of occluding accessories Frequent occlusion between pedestrians Despite the challenges, pedestrian detection still remains an active research area in computer vision in recent years. Numerous approaches have been proposed. Detectors are trained to search for pedestrians in the video frame by scanning the whole frame. The detector would “fire” if the image features inside the local search window meet certain criteria. Some methods employ global features such as edge template others uses local features like histogram of oriented gradients descriptors. The drawback of this approach is that the performance can be easily affected by background clutter and occlusions. Pedestrians are modeled as collections of parts. Part hypotheses are firstly generated by learning local features, which include edgelet and orientation features. These part hypotheses are then joined to form the best assembly of existing pedestrian hypotheses. Though this approach is attractive, part detection itself is a difficult task. 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. In 2005, Leibe et al. proposed an approach combining both the detection and with the name Implicit Shape Model (ISM). A codebook of local appearance is learned during the training process.