The particle filtering technique with multiple cues such as colour, texture and edges as observation features is a powerful technique for tracking deformable objects in image sequences with complex backgrounds. In this paper, our recent work (Brasnett et al., 2005) on single object tracking using particle filters is extended to multiple objects. In the proposed scheme, track initialisation is embedded in the particle filter without relying on an external object detection scheme. The proposed scheme avoids the use of hybrid state estimation for the estimation of number of active objects and its associated state vectors as proposed in (Czyz et al., 2005). The number of active objects and track management are handled by means of probabilities of the number of active objects in a given frame. These probabilities are shown to be easily estimated by the Monte Carlo data association algorithm used in our algorithm. The proposed particle filter (PF) embeds a data association technique based on the joint probabilistic data association (JPDA) which handles the uncertainty of the measurement origin. The algorithm is able to cope with partial occlusions and to recover the tracks after temporary loss. The probabilities calculated for data associations take part in the calculation of probabilities of the number of objects. We evaluate the performance of the proposed filter on various real-world video sequences with appearing and disappearing targets.
|Translated title of the contribution||Multiple object tracking using particle filters|
|Title of host publication||2006 IEEE Aerospace Conference, Big Sky, MT, United States|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Publication status||Published - Mar 2006|
|Event||IEEE Aerospace Conference - Big Sky, MT, United States|
Duration: 1 Mar 2006 → …
|Conference||IEEE Aerospace Conference|
|City||Big Sky, MT|
|Period||1/03/06 → …|
Bibliographical noteRose publication type: Conference contribution
Sponsorship: The authors are grateful for the financial support from the UK MOD Data and Information Fusion Defence Technology Centre.
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