Normalized Autobinomial Markov Channels For Pedestrian Detection

Pedestrian detection represents one of the most important components of engineering devices that use automated vision to help decision systems take quick and accurate actions. Such systems are defined and customized to be useful for different needs, such as monitoring and aided surveillance, or increasing safety features in automotive industry. Given the large spectrum of applications that use pedestrian detection, demand has increased in recent years for the development of feasible solutions which can be integrated in devices such as smartphones or action cameras. This paper focuses on finding probabilistic features that highlight the human body characteristics regardless of contextual information in images. Adjacent pixels are often spatially correlated, which means that they are likely to have similar values. We view the image as a collection of random variables indexed by certain locations, called sites. The state of a site ξ is conditionally independent of all variables in the random field, except the neighbouring system Nξ = { η ∈Ω | η 6= ξ , d2(ξ ,η)≤ ∆ } , where ∆ is a positive integer and d2(ξ ,η) is the squared Euclidean distance between ξ and η . The neighbouring system strictly depends on a collection of cliques C = ∑ ) k=1 Ck, where ω(∆) is the number of cliques for each local specification. Energy function: An unpublished manuscript [2] describes how to interpret the local property of a Markov random field in terms of energy and potential, claiming that the probability at a site ξ is given by: