Moving object detection from a moving stereo camera via depth information and visual odometry

It is important for driving safety to know whether surrounding objects are moving or static. Most existing methods use pre-trained object detectors to detect vehicles and humans before determine whether they are moving or not. However, there are not only these two type objects on the real road. This study presents a system using depth information and visual odometry to detect moving object. It also adopts an adaptive thresholding method to enhance performance on detection. The proposed system can accurately detect moving objects according to the experimental results.

[1]  T. Poggio,et al.  Direction estimation of pedestrian from multiple still images , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[2]  Hyung Jin Chang,et al.  Detection of Moving Objects with Non-stationary Cameras in 5.8ms: Bringing Motion Detection to Your Mobile Device , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[3]  Truong Q. Nguyen,et al.  Moving Object Detection With a Freely Moving Camera via Background Motion Subtraction , 2017, IEEE Transactions on Circuits and Systems for Video Technology.

[4]  Song Zheng,et al.  An Improved Moving Object Detection Algorithm Based on Frame Difference and Edge Detection , 2007, Fourth International Conference on Image and Graphics (ICIG 2007).

[5]  Andreas Geiger,et al.  Visual odometry based on stereo image sequences with RANSAC-based outlier rejection scheme , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[6]  Takashi Naito,et al.  Pedestrian detection and direction estimation by cascade detector with multi-classifiers utilizing feature interaction descriptor , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[7]  Rita Cucchiara,et al.  Detecting Moving Objects, Ghosts, and Shadows in Video Streams , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Julius Ziegler,et al.  Sparse scene flow segmentation for moving object detection in urban environments , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[9]  Wei Liu,et al.  A Novel Motion Detection Approach for Large FOV Cameras , 2010, AICI.

[10]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[11]  Reinhard Klette,et al.  Integrated Pedestrian and Direction Classification Using a Random Decision Forest , 2013, 2013 IEEE International Conference on Computer Vision Workshops.

[12]  Hong Liu,et al.  Online person orientation estimation based on classifier update , 2015, 2015 IEEE International Conference on Image Processing (ICIP).