By David Gerónimo, Antonio M. López
Pedestrian defense structures (PPSs) are on-board structures geared toward detecting and monitoring humans within the atmosphere of a motor vehicle on the way to stay away from very likely risky events. those structures, including different complex driving force assistance platforms (ADAS) resembling lane departure caution or adaptive cruise keep an eye on, are some of the most promising how one can enhance site visitors protection. by means of computing device imaginative and prescient, cameras operating both within the seen or infra-red spectra were tested as a competent sensor to accomplish this job. however, the variety of human’s visual appeal, not just when it comes to garments and sizes but in addition due to their dynamic form, makes pedestrians essentially the most advanced periods even for desktop imaginative and prescient. in addition, the unstructured altering and unpredictable setting within which such on-board platforms needs to paintings makes detection a tricky activity to be performed with the demanded robustness. during this short, the state-of-the-art in PPSs is brought during the evaluate of the main appropriate papers of the decade. a typical computational structure is gifted as a framework to prepare each one procedure in accordance with its major contribution. greater than three hundred papers are referenced, such a lot of them addressing pedestrian detection and others such as the descriptors (features), pedestrian types, and studying machines used. additionally, an outline of themes similar to real-time elements, structures benchmarking and destiny demanding situations of this examine zone are presented.
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Extra info for Vision-based Pedestrian Protection Systems for Intelligent Vehicles
16). Of course, both things are also possible. In Sect. 2 we have already seen examples of combining different descriptors using a single modality. For instance, we have mentioned the concatenation of HOG and LBP features that are feed into linear SVM . Usually it is said that HOG captures shape information while LBP captures texture information. Similarly, we have mentioned the combination of EOH and Haar-like features in a pool mined by some AdaBoost variant [51, 52, 120, 122, 123, 186].
139] divide the CoHOG feature vector into many smaller ones and construct a rejection-cascade with weak classifiers based on such smaller vectors. However, CoHOG still throws away potentially relevant information, the gradient magnitude. Accordingly, Pang et al.  modify CoHOG to account for gradient magnitude during the co-occurrence voting process. For each matrix co-occurrence entry two gradient orientation are involved, so two corresponding gradient magnitudes too. Therefore, several strategies are considered to define the amount of gradient magnitude to accumulate.
Thus, the histograms of cell-structured LBPs  being cell-likelihood functions of the co-occurrence features (LBPs). Mita et al.  follow this method of capturing co-occurrences for face and hand detection. The single features are Haar-like ones, from them a vast amount of co-occurrence features can be generated. Note that given an amount of single features to combine, many possible combinations are generated since the Haar-like features vary in position and scale. In addition, different amounts of combined features are considered.