Tracking multiple objects in non-stationary video

One of the key problems in computer vision and pattern recognition is tracking. Multiple objects, occlusion, and tracking moving objects using a moving camera are some of the challenges that one may face in developing an effective approach for tracking. While there are numerous algorithms and approaches to the tracking problem with their own shortcomings, a less-studied approach considers swarm intelligence. Swarm intelligence algorithms are often suited for optimization problems, but require advancements for tracking objects in video. This paper presents an improved algorithm based on Bacterial Foraging Optimization in order to track multiple objects in real-time video exposed to full and partial occlusion, using video from both fixed and moving cameras. A comparison with various algorithms is provided. Track: Real World Application Categories and Subject Descriptors I.4.8 [Image Processing and Computer Vision]: Scene Analysis—Tracking; I.5.4 [Pattern Recognition]: Applications—...

• Type: Conference
• Year: 2009
• Where: GECCO
• Authors: Hoang Nguyen, Bir Bhanu
• Keywords: Bacterial Foraging Optimization, GECCO 2009, Optimization, Swarm Intelligence, Tracking Moving Objects