A mobile robot needs to perceive the motions of external objects to perform tasks successfully in a dynamic environment. We propose a set of algorithms for multiple motion tracking from a mobile robot equipped with a monocular camera and a laser rangefinder. The key challenges are 1. to compensate the ego-motion of the robot for external motion detection, and 2. to cope with transient and structural noise for robust motion tracking. In our algorithms, the robot ego-motion is directly estimated using corresponding feature sets in two consecutive images, and the position and velocity of a moving object is estimated in image space using multiple particle filters. The estimates are fused with the depth information from the laser rangefinder to estimate the partial 3D position. The proposed algorithms have been tested with various configurations in outdoor environments. The algorithms were deployed on three different platforms; it was shown that various type of ego-motion were successfully eliminated and the particle filter was able to track motions robustly. The multiple target tracking algorithm was tested for different types of motions, and it was shown that our multiple lter approach is effective and robust. The tracking algorithm was integrated with a robot control loop, and its realtime capability was demonstrated.