Moving object detection

Moving object detection is a technique used in computer vision and image processing. Multiple consecutive frames from a video are compared by various methods to determine if any moving object is detected.

Moving objects detection has been used for wide range of applications like video surveillance, activity recognition, road condition monitoring, airport safety, monitoring of protection along marine border, etc.^[1]

Definition

Moving object detection is to recognize the physical movement of an object in a given place or region.^[2] By acting segmentation among moving objects and stationary area or region,^[3] the moving objects' motion can be tracked and thus analyzed later. To achieve this, consider a video is a structure built upon single frames, moving object detection is to find the foreground moving target(s), either in each video frame or only when the moving target shows the first appearance in the video.^[4]

Traditional methods

Among all the traditional moving object detection methods, we could categorize them into four major approaches: Background subtraction, Frame differencing, Temporal Differencing, and Optical Flow.^[2]

Frame differencing

Instead of using traditional approach, to use image subtraction operator by subtracting second and images afterwards, the frame differencing method makes comparisons between two successive frames to detect moving targets.^[5]

Temporal differencing

The temporal differencing method identifies the moving object by applying pixel-wise difference method with two or three consecutive frames.^[3]

References

^ Chaquet, Jose M.; Carmona, Enrique J.; Fernández-Caballero, Antonio (June 2013). "A survey of video datasets for human action and activity recognition". Computer Vision and Image Understanding. 117 (6): 633–659. doi:10.1016/j.cviu.2013.01.013. hdl:10578/3697.
^ ^a ^b [1], J. S. Kulchandani and K. J. Dangarwala, "Moving object detection: Review of recent research trends," 2015 International Conference on Pervasive Computing (ICPC), Pune, 2015, pp. 1-5. doi: 10.1109/PERVASIVE.2015.7087138.
^ ^a ^b [2], Weiming Hu, Tieniu Tan, Liang Wang, and Steve Maybank, “A Survey on Visual Surveillance of Object Motion and Behaviors,” IEEE Trans. on Systems, Man, and Cybernetics—Part C: Applications and Reviews, vol. 34, no. 3, pp. 334-352, August 2004.
^ [3], Bahadir Karasulu and Serdar Korukoglu (2013). Performance Evaluation Software: Moving Object Detection and Tracking in Videos.
^ [4], Jain, R. and H. Nagel, “On the Accumulative Difference Pictures for the Analysis of Real World Scene Sequences,” IEEE Tran. on Pattern Anal. Mach. Intell., pp. 206-221, 1979.

[1] Chaquet, Jose M.; Carmona, Enrique J.; Fernández-Caballero, Antonio (June 2013). "A survey of video datasets for human action and activity recognition". Computer Vision and Image Understanding. 117 (6): 633–659. doi:10.1016/j.cviu.2013.01.013. hdl:10578/3697.

[moving_review-2] [1], J. S. Kulchandani and K. J. Dangarwala, "Moving object detection: Review of recent research trends," 2015 International Conference on Pervasive Computing (ICPC), Pune, 2015, pp. 1-5. doi: 10.1109/PERVASIVE.2015.7087138.

[survey-3] [2], Weiming Hu, Tieniu Tan, Liang Wang, and Steve Maybank, “A Survey on Visual Surveillance of Object Motion and Behaviors,” IEEE Trans. on Systems, Man, and Cybernetics—Part C: Applications and Reviews, vol. 34, no. 3, pp. 334-352, August 2004.

[4] [3], Bahadir Karasulu and Serdar Korukoglu (2013). Performance Evaluation Software: Moving Object Detection and Tracking in Videos.

[5] [4], Jain, R. and H. Nagel, “On the Accumulative Difference Pictures for the Analysis of Real World Scene Sequences,” IEEE Tran. on Pattern Anal. Mach. Intell., pp. 206-221, 1979.

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