Theodore Camus

Reliable and fast eye finding in close-up images

This paper describes a method for quickly and robustly localizing the iris and pupil boundaries of a human eye in close-up images. Such an algorithm can be critical for iris identification, or for applications that must determine the subjects gaze direction, e.g., human-computer interaction or driver attentiveness determination. A multi-resolution coarse-to-fine search approach is used, seeking to maximize gradient strengths and uniformities measured across rays radiating from a candidate iris or pupils central point. An empirical evaluation of 670 eye images, both with and without glasses, resulted in a 98% localization accuracy. The algorithm has also shown robustness to weak illumination and most specular reflections (e.g., at eyewear and cornea), simplifying system component requirements. Rapid execution is achieved on a 750 MHz desktop processor.

Collision Sensing by Stereo Vision and Radar Sensor Fusion

To take the advantages of both stereo cameras and radar, this paper proposes a fusion approach to accurately estimate the location, size, pose and motion information of a threat vehicle with respect to the host from observations obtained by both sensors. To do that, we first fit the contour of a threat vehicle from stereo depth information, and find the closest point on the contour from the vision sensor. Then the fused closest point is obtained by fusing radar observations and the vision closest point. Next by translating the fitted contour to the fused closest point, the fused contour is obtained. Finally the fused contour is tracked by using the rigid body constraints to estimate the location, size, pose and motion of the threat vehicle. Experimental results from both the synthetic data and the real world road test data demonstrate the success of the proposed algorithm.

Collision sensing by stereo vision and radar sensor fusion

To take the advantages of both stereo cameras and radar, this paper proposes a fusion approach to accurately estimate the location, size, pose and motion information of a threat vehicle with respect to the host from observations obtained by both sensors. To do that, we first fit the contour of a threat vehicle from stereo depth information, and find the closest point on the contour from the vision sensor. Then the fused closest point is obtained by fusing radar observations and the vision closest point. Next by translating the fitted contour to the fused closest point, the fused contour is obtained. Finally the fused contour is tracked by using the rigid body constraints to estimate the location, size, pose and motion of the threat vehicle. Experimental results from both the synthetic data and the real world road test data demonstrate the success of the proposed algorithm.

Stereo-Based Object Detection, Classi?cation, and Quantitative Evaluation with Automotive Applications

A real-time stereo-based pre-crash object detection and classification system is presented. The system employs a model based stereo object detection algorithm to find candidate objects from the scene, followed by a Bayesian classification framework to assign each candidate to its proper class. Our current system detects and classifies several types of objects commonly seen for automotive applications, namely vehicles, pedestrians/bikes, and poles. We describe both the detection and classification algorithms in detail along with real-time implementation issues. A quantitative analysis of performance on a static data set is also presented.

Stereo-based vision system for automotive imminent collision detection

Imminent collision detection is an important functionality in the area of automotive safety. In the event that an unavoidable collision can be detected in advance of the actual impact, various measures can be taken to mitigate injury and damage. In this paper, we demonstrate that stereo vision is a promising solution to this problem. Our prototype system has been rigorously tested for different colliding scenarios (e.g., different intersection angles and different travelling speeds), including live tests in an industrial crash-test facility. We explain the novel algorithms behind the system, including an algorithm for detecting objects in depth images, and algorithms for estimating the travelling velocity of detected vehicles. Quantitative results and representative examples are also included.

Towards a Practical Stereo Vision Sensor

Development of a practical stereo vision sensor for real-world applications must account for the variability of high-volume production processes and the impact of unknown environmental conditions during its operation. One critical factor of stereo depth estimation performance is the relative alignment of the stereo camera pair. While imperfectly aligned stereo cameras may be rectified in the image domain, there are some errors introduced by both the calibration recovery and image rectification processes. Finally, additional uncalibrated misalignments, for example due to thermal or mechanical deformation in a harsh automotive environment, may occur which will further deteriorate stereo depth estimation. This paper describes an experimental framework for determining these limits using image processing algorithms, operating on graphically synthesized imagery, with performance envelope validation on real stereo image data.