This work proposes and implements a method based on Context-Aware Visual Attention Model (CAVAM), but modifying the method in such way that the detection algorithm is replaced by Histograms of Oriented Gradients (HOG). After reviewing different algorithms for people detection, we select HOG method because it is a very well known algorithm, which is used as a reference in virtually all current research studies about automatic detection. In addition, it produces accurate results in significantly less time than many algorithms. In this way, we show that CAVAM model can be adapted to other methods for object detection besides Scale-Invariant Feature Transform (SIFT), as it was originally proposed. Additionally, we use TUD dataset image sequences to evaluate and compare our approach with the original HOG algorithm. These experiments show that our method achieves around 2x speed-up at just 2% decreased accuracy. Moreover, the proposed approach can improve precision and specificity by more than 2%.