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

Video

Video is the technology of electronically capturing, recording, processing, storing, transmitting, and reconstructing a sequence of still images representing scenes in motion.- History :...

 footages.

Challenges for pedestrian detection

• Various style of clothing in appearance
• Different possible articulations
• The presence of occluding accessories
• Frequent occlusion between pedestrians

Existing Approaches

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

Computer vision

Computer vision is a field that includes methods for acquiring, processing, analysing, and understanding images and, in general, high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the forms of decisions...

 in recent years. Numerous approaches have been proposed.

Holistic Detection

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

Histogram of oriented gradients

Histogram of Oriented Gradients are feature descriptors used in computer vision and image processing for the purpose of object detection. The technique counts occurrences of gradient orientation in localized portions of an image...

descriptors. The drawback of this approach is that, the performance can be easily affected by background clutter and occlusions.

Part-based Detection

Pedestrians are modeled as collections of parts. Part hypotheses are firstly generated by learning local features, which includes edgelet features the orientation features , and etc. 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.

Patch-based Detection

Recently Leibe et al. proposed an approach combining both the detection and segmentation with the name Implicit Shape Model (ISM). A codebook of local appearance is learned during the training process. In the detecting process, extracted local features are used to match against the codebook entries, and each match casts one vote for the pedestrian hypotheses. Final detection results can be obtained by further refining those hypotheses. The advantage of this approach is only a small number of training images are required.

Detection using Multiple Cameras

Fleuret et al. suggested a method for integrating multiple calibrated cameras for detecting multiple pedestrians. In this approach, The ground plane is partitioned into uniform, non-
overlapping grid cells, typically with size of 25 by 25 (cm). The detector produces a Probability Occupancy Map (POM), it provides an estimation of the probability of each grid cell to be occupied by a person.

External links

• http://cvlab.epfl.ch/software/pom - Code for POM - Pedestrian Detection from multiple cameras using Probabilistic Occupancy Map