We present a novel detection based particle filtering framework for real-time multi-object tracking (MOT). It integrates object detection and motion information with particle filter detecting and tracking the multiple objects dynamically and simultaneously. To demonstrate the approach, we concentrate on the complex multi-head tracking while the framework is general for any kind of objects. Three novel contributions are made: 1) Distinct with the conventional particle filter which generates particles from the prior density, we propose a novel importance function based on up to date detection and motion observation which is much closer to the desired posterior. 2) By integrating detection, the tracker can do the initialization automatically, handle new object appearance and hard occlusion for MOT. By using motion estimation, it can track fast motion activities. 3) Hybrid observations including color and detection information are used to calculate the likelihood which makes the approach more stable. The proposed method is superior to the available tracking methods for multi-head tracking and can handle not only the changes of scale, lighting, zooming, and orientation, but also fast motion, appearance, and hard occlusion.