The University of Texas at Austin Vision-Based Pedestrian Detection for Driving Assistance

1. The University of Texas at AustinVision-Based Pedestrian Detection for Driving Assistance Marco Perez

2. Background • The emergence of a set of vehicle capabilities centered around the notion of driver assistance. • These involve sensor-based systems, which continuously evaluate the surroundings of a vehicle, displaying relevant information to the driver and sometimes taking control of the vehicle. • The objective of these systems is to increase the safety, convenience and efficiency of driving. • Different sensors may provide the “eyes” and “ears” of driver assistance systems: • video cameras • radar sensors • laser scanners • ultrasound devices • Vision systems in the visible spectrum seem the most attractive solution. • Video sensors (cameras) provide texture information at very fine angular resolution, allowing the high degree of discrimination necessary for object recognition (lanes, vehicles, pedestrians, traffic signs, traffic lights). • The human visual system is the best example of what performance may be achieved with such sensors, if only the appropriate processing is added.

3. Background • “Visual detection of pedestrians from a moving vehicle” • The objective is to detect dangerous situations involving pedestrians ahead of time. • A challenging problem for the following reasons: • Pedestrians appear in highly cluttered/uncontrolled backgrounds. • In order to obtain interesting foreground regions containing pedestrians, it is not possible to apply common background subtraction methods due to the moving camera. • Wide range of appearances (body size, pose, clothing, light conditions). • Sometimes pedestrians will be far away from the camera, appearing small in the image (at low resolution).

4. Key Paper #1(Zhao & Thorpe, 2000) • It runs in real-time. • Employs a stereo vision system to provide range information for foreground/background segmentation. • Only concerned about objects close to the vehicle. Hence, detected background objects are eliminated from the disparity map by range thresholding. • Small regions are eliminated through size thresholding. The size range of a normal person is obtained from statistic data. • Sub-images are converted to intensity gradient to encode shape information. • Intensity gradient images are inputs of a trained (back-propagation) neural network for pedestrian recognition: • 5318 training data: 1012 of pedestrians and 4306 of objects. • Experiments performed on a large number of urban street scenes: • Detection rate: 85.2% • False alarm rate: 3.1%

5. Key Paper #2(Gavrila, Giebel & Munder 2004) • Template matching based on contour features to find candidate solutions. • Shape matching based on Distance Transforms. • A verification method based on a Radial Basis Function is used to dismiss false positives. • Experimental results on pedestrian detection off-line and in real time.

6. Key Paper #3(Bertozzi, Broggi, Fascioli, Tibaldi, Chapuis & Chausse, 2004) • Recognizes pedestrians in different environments and localizes them with the use of a Kalman filter estimator configured as a tracker. • Pedestrians are first recognized through the use of edge density and symmetry maps. • The former information is passed on to the tracker module which reconstructs an interpretation of the pedestrian positions in the scene