Low-Level feature detectors and descriptors for smart image and video analysis: a comparative study

Local feature detectors and descriptors (hereinafter extractors) play a key role in the modern computer vision. Their scope is to extract, from any image, a set of discriminative patterns (hereinafter keypoints) present on some parts of background and/or foreground elements of the image itself. A prerequisite of a wide range of practical applications (e.g., vehicle tracking, person re-identification) is the design and development of algorithms able to detect, recognize and track the same keypoints within a video sequence. Smart cameras can acquire images and videos of an interesting scenario according to different intrinsic (e.g., focus, iris) and extrinsic (e.g., pan, tilt, zoom) parameters. These parameters can make the recognition of a same keypoint between consecutive images a hard task when some critical factors such as scale, rotation and translation are present. The aim of this chapter is to provide a comparative study of the most used and popular low-level local feature extractors: SIFT, SURF, ORB, PHOG, WGCH, Haralick and A-KAZE. At first, the chapter starts by providing an overview of the different extractors referenced in a concrete case study to show their potentiality and usage. Afterwards, a comparison of the extractors is performed by considering the Freiburg-Berkeley Motion Segmentation (FBMS-59) dataset, a well-known video data collection widely used by the computer vision community. Starting from a default setting of the local feature extractors, the aim of the comparison is to discuss their behavior and robustness in terms of invariance with respect to the most important critical factors. The chapter also reports comparative considerations about one of the basic steps based on the feature extractors: the matching process. Finally, the chapter points out key considerations about the use of the discussed extractors in real application domains.