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In this paper, we develop a multi-human detection system with a team of robots basically in an indoor environment. To start with, we propose a hybrid approach to resolve the problem of human leg detection using Laser Range Finder (LRF) for each robot, that returns not only “true” or “false” type of answer but also a probability. Specifically, the set of measurement data obtained from the laser range finder mounted on a robot is further decomposed into several sectors using an appropriate segmentation technique. Then, we apply a probabilistic model to compare these sectors with leg patterns to check if any of them belongs to the set of human leg patterns or not. Next, we examine the promising leg sectors with a modified Inscribe Angle Variance (IAV) method in order to confirm if these sectors are from human leg's arc feature or not. Moreover, we also use motion detector to check if these objects move or not as an enhancement of the detection. For the entire multi-human detection system, each robot of the team delivers the detected human information to our central control computer through the Inter-Process Communication (IPC). With prior map information of the residing environment and supposing each robot in the team has a localization module, we can then map these results of human detection from every robot into their global coordinates after process of data association. But in order to reduce the computational complexity while doing the data association among these robots in a team, we introduce a set of appropriate rules. Finally, we apply a particle filter based tracking algorithm to keep accurate track of people being detected and to improve the robustness of the detection outcome. This work has been evaluated through several experiments with a number of mobile robots and humans in an indoor environment, and promising performance has been observed.