  Sliding window approaches have been widely used for object recognition tasks
in recent years. They guarantee an investigation of the entire input image for
the object to be detected and allow a localization of that object. Despite the
current trend towards deep neural networks, sliding window methods are still
used in combination with convolutional neural networks. The risk of overlooking
an object is clearly reduced compared to alternative detection approaches which
detect objects based on shape, edges or color. Nevertheless, the sliding window
technique strongly increases the computational effort as the classifier has to
verify a large number of object candidates. This paper proposes a sliding
window approach which also uses depth information from a stereo camera. This
leads to a greatly decreased number of object candidates without significantly
reducing the detection accuracy. A theoretical investigation of the
conventional sliding window approach is presented first. Other publications to
date only mentioned rough estimations of the computational cost. A mathematical
derivation clarifies the number of object candidates with respect to parameters
such as image and object size. Subsequently, the proposed disparity sliding
window approach is presented in detail. The approach is evaluated on pedestrian
detection with annotations and images from the KITTI object detection
benchmark. Furthermore, a comparison with two state-of-the-art methods is made.
Code is available in C++ and Python https://github.com/julimueller/
disparity-sliding-window.
