Video surveillance systems are employed to prevent crime, mounting hundreds of cameras and sensors monitoring activities during the whole day. Due to the huge amount of video information generated in real time, these surveillance centers are requiring more technology and intelligence to support human operators in many complex situations. There are important analyses that could be realized with this video-data: from criminalistics event detection to particular object recognition. One important tool is License Plate Recognition (LPR) that helps detecting vehicles that could have been robbed. Although corporative solutions exist, these techniques require a lot of processing power and special located cameras, that not always could be afford by the local government. In this context, the proposed project is based on applying open-source LPR algorithms that runs on already existent surveillance cameras. These cameras are observing a complete scene (not just a line as it is commonly used), so LPR algorithms are rather slow, processing only 1 image per second. For this reason, the objective is to improve the performance combining a parallel LPR running on graphic processor units (GPU) and object tracking algorithms. This work describes the ongoing implementation, the techniques currently used for object tracking and LPR implementation, and exposes results regarding the efficiency of the solution.