Computer vision is used to assist or perform humans in performing many functions. Computer vision can be more accurate and better suited than human vision in many applications. For example, computer vision for surveillance applications can better detect moving objects and recognize shapes for indefinite periods of time simply using a set of cameras in conjunction with various computer algorithms (e.g., background subtraction).
Some applications require a computer vision system able to operate from a moving platform. Such a system needs to deal with additional factors due to the system's own motion. Conventional approaches for these applications rely on mobile cameras and other hardware/software with computer based algorithms. Some commonly used algorithms include, for example, pattern classifiers such as Support Vector Machine (“SVM”), neural nets for shape identification, motion patterns such as periodicity, probabilistic modeling of pose as well as chamfer distance calculation, plane and parallax decomposition, and the like. All these conventional techniques can be used to identify objects of interest in a moving scene. In general, in these approaches the static scene viewed by a moving camera has a motion induced by the movement of the system. These methods are based on the observation that moving objects have movements that are not consistent with that of the background. After compensating for the system-induced motion, the remaining motion must come from independently moving objects. However, one drawback of these approaches for use in real-time vision systems is that they often involve explicit ego-motion computation or a motion/flow constraint. Ego-motion calculations are computationally expensive and severely impact the performance of real-time vision systems.
Other applications require a computer vision system able to operate under low illumination conditions. The assistance of computer vision in low illumination conditions is particularly important for humans because human vision is weaker at night or under low illumination conditions. Conventional approaches to night vision systems generally rely on infrared-sensitive cameras. The appearance of the scene taken at night by an infrared-sensitive camera is quite different from that taken by a regular camera during daytime. Because infrared cameras are heat sensitive, heat-emitting objects appear brighter than surrounding objects. For example, for human detection applications, most approaches exploit the heat-emitting characteristic of human bodies in conjunction with infrared-sensitive cameras. Conventional techniques used in infrared night vision applications include probabilistic templates for pedestrians, appearance-based learning, shape-based comparison, methods using symmetry, histograms, and the like.
One important application of computer night vision from a moving platform is pedestrian detection systems in vehicular applications. Pedestrian detection methods use multiple steps to locate the pedestrian in the scene. These methods generally operate by finding a large number of candidates for human-like blobs in the scene. Then, these candidates are filtered into human and non-human categories based on certain criteria such as location, size, and road information. For example, one such approach to pedestrian detection is described in “Pedestrian Detection and Tracking with Night Vision,” F. Xu and K. Fujimura, Proc. of the IEEE Trans. on Intelligent Vehicles Symposium, Versailles, France (June 2002), incorporated herein by reference in its entirety. However, the filtering process is complicated and subject to failure because in practice it is difficult to enumerate all possible shapes humans can take. Also, from the viewpoint of automotive safety, it is important to detect moving objects that cross the vehicle's path, regardless of whether that is a human or some other object.
Thus, there is a need for moving object detection systems and methods for operating under low illumination conditions and from a moving platform that (1) can detect moving objects with any shape and (2) do not require complicated ego-motion calculations.
USPTO Backgrounds
USPTO Backgrounds