Advancement in technology has given rise to the need for technology to be utilized in extracting meaningful game-play information from sports. To do so in a ball-game like squash, the prime objective is to perform ball-tracking in an adequate and efficient manner. In squash, ball-tracking is complex due to the small size of the ball, the high-speed movement and constant occlusion due to the continuous movement of the players. The current state-of-the-art ball tracking methods use high-speed cameras along with high-computation power resources to solve these problems in similar sports such as tennis. The aim of this thesis is to solve the challenges in ball-tracking for squash using a low-cost approach with low-computation power resources and a single camera view. A ball-detection system with a high-accuracy and a ball-tracking system which can optimally tackle the problem of occlusion is developed using computer-vision techniques and by utilizing the cues from the game itself. The implementation is carried out on a Raspberry-Pi which is characterized as a low-computation platform with an Arm Cortex-A53 processor. The results show that the tracking-problem can be solved using a low-cost approach for the challenging scenarios that are present in squash. The 2D trajectory of the ball generated as a result can be used for various applications such as line-calling, shot analysis and game analysis.