Various human detection algorithms are limited in capability due to the lack of using other supplemental algorithms for enhancing detection. We propose using two different algorithms to extract vital information to augment human detection algorithms for increased accuracy. The first algorithm is the computation of depth information. Information needed to obtain depth is based on the specific location of the camera based from frame to frame. Most calibrated stereo cameras can develop accurate depth information, but the motion that takes place from frame to frame can be utilized for developing rough depth perception of the objects in the scene. Block-matching and optical flow algorithms can be used to provide these disparities that happen in the image, which will provide depth information for the human detection algorithm. The second algorithm is superpixel segmentation. This algorithm determines a rough over-segmentation of the imagery, which well defines the boundaries as larger pixels that are within the imagery. This information can be used to distinguish background and foreground information to create a specific segmentation around the human detection, rather than a bounding box detection that may include various background information. The fusion of these algorithms with human detection has been shown to increase detection accuracy and providing better localization of the human in the imagery.
Hussin K. Ragb, Theus H. Aspiras, Vijayan K. Asari, "Depth and Superpixel Extraction for Augmenting Human Detection" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Imaging and Multimedia Analytics in a Web and Mobile World, 2018, pp 336-1 - 336-6, https://doi.org/10.2352/ISSN.2470-1173.2018.10.IMAWM-336