© 2014 SPIE. In recent years tracking applications with development of new technologies like smart TVs, Kinect, Google Glass and Oculus Rift become very important. When tracking uses a matching algorithm, a good prediction algorithm is required to reduce the search area for each object to be tracked as well as processing time. In this work, we analyze the performance of different tracking algorithms based on prediction and matching for a real-time tracking multiple objects. The used matching algorithm utilizes histograms of oriented gradients. It carries out matching in circular windows, and possesses rotation invariance and tolerance to viewpoint and scale changes. The proposed algorithm is implemented in a personal computer with GPU, and its performance is analyzed in terms of processing time in real scenarios. Such implementation takes advantage of current technologies and helps to process video sequences in real-time for tracking several objects at the same time.
|Original language||American English|
|State||Published - 1 Jan 2014|
|Event||Proceedings of SPIE - The International Society for Optical Engineering - |
Duration: 1 Jan 2015 → …
|Conference||Proceedings of SPIE - The International Society for Optical Engineering|
|Period||1/01/15 → …|
Miramontes-Jaramillo, D., Kober, V., & Diaz-Ramirez, V. H. (2014). Multiple objects tracking with HOGs matching in circular windows. Paper presented at Proceedings of SPIE - The International Society for Optical Engineering, . https://doi.org/10.1117/12.2061246