This paper addresses the problem of detecting and tracking multiple moving people when the scene background is not known in advance. We have proposed a new background detection technique for dynamic environment that learns and models the scene background based on K-mean clustering technique and pixel statistics. The background detection is achieved using the first frames of the scene where, the number of these frames needed depends on how dynamic is the observed environment. We have also proposed a new feature-based framework, which requires feature extraction and feature matching, for tracking moving people. We have considered color, size, blob bounding box and motion information as features of people. In our feature-based tracking system, we have used Pearson correlation coefficient for matching feature-vector with temporal templates. The occlusion problem has been solved by sub-blobbing. Our tracking system is fast and free from assumptions about human structure. The tracking system has been implemented using Visual C++ and OpenCV and tested on real-world videos. Experimental results suggest that our tracking system achieved good accuracy and can process videos close to real-time.