Human Detection Using Oriented Histograms of Flow and Appearance

Detecting humans in films and videos is a challenging problem owing to the motion of the subjects, the camera and the background and to variations in pose, appearance, clothing, illumination and background clutter. We develop a detector for standing and moving people in videos with possibly moving cameras and backgrounds, testing several different motion coding schemes and showing empirically that orientated histograms of differential optical flow give the best overall performance. These motion-based descriptors are combined with our Histogram of Oriented Gradient appearance descriptors. The resulting detector is tested on several databases including a challenging test set taken from feature films and containing wide ranges of pose, motion and background variations, including moving cameras and backgrounds. We validate our results on two challenging test sets containing more than 4400 human examples. The combined detector reduces the false alarm rate by a factor of 10 relative to the b...

• Type: Conference
• Year: 2006
• Where: ECCV
• Authors: Navneet Dalal, Bill Triggs, Cordelia Schmid
• Keywords: Challenging Test Sets, Computer Vision, ECCV 2006, False Alarm Rate, False Alarm Rates, Gradient Appearance Descriptors, Motion Coding Schemes