Trail optimization framework to detect nonlinear object motion in video sequences

• Published in: Signal, image and video processing image and video processing, 2020-04, Vol.14 (3), p.537-545
• Main Authors: Manonmani, T., Pushparaj, Vijayakumari
• Format: Article
• Language: English
• Subjects: Algorithms; Center of gravity; Computer Imaging; Computer Science; Image Processing and Computer Vision; Kalman filters; Motion perception; Motion simulation; Moving object recognition; Multimedia Information Systems; Optimization; Original Paper; Pattern Recognition and Graphics; Service robots; Signal,Image and Speech Processing; Surveillance systems; Tracking problem; Tracks (paths); Vision
• ISSN: 1863-1703; 1863-1711
• DOI: 10.1007/s11760-019-01581-7

Detecting and tracking multiple moving objects in a sequence of video images is an important application in automated surveillance systems, service robots, target discrimination, traffic monitoring, etc. Since these systems require real-time processing, providing an efficient method with lower computational complexity is a challenge. Besides the combination of rudimentary techniques which works well for linear objects, this paper presents an operative technique to recognize and track multiobject moving in a video sequence without using additional trackers. Instead of processing the entire video sequence, candidate keyframes are identified and the foreground moving objects are detected. To solve the nonlinear tracking problem, this paper presents an optimized twin algorithm—amended Kalman with Hungarian algorithm—to track multiple moving objects on their center of gravity in the minimal bounding box. Kalman filter predicts the location of the foreground objects in various orientation bins to extract the short-term movement of the foreground objects in possible trajectory paths. Hungarian algorithm locates the presence/absence of nonlinear objects in the tracks. Location prediction and tracking of moving objects only on the candidate frames reduces the computation time. Therefore, it is a good alternative method for nonlinear motion estimation that is likely required for multiobject identity tracking in image sequences. This approach achieves high accuracy and reduces additional computations comparable to state-of-the-art online trackers. The comparison also proved that the proposed method has better precision–recall values with computation simplicity.