Modern day intelligent transportation system (ITS) relies on reliable and accurate estimated traffic parameters. Travel speed, traffic flow, and traffic state classification are the main traffic parameters of interest. These parameters can be estimated through efficient vision-based algorithms and appropriate camera sensor technology. With the advances in camera technologies and increasing computing power, use of monocular vision, stereo vision, and camera sensor fusion technologies have been an active research area in the field of ITS. In this thesis, we investigated stationary monocular and stereo camera technology for traffic parameters estimation. Stationary camera sensors provide large spatial-temporal information of the road section with relatively low installation costs. Two novel scientific contributions for vehicle detection and recognition are proposed. The first one is the use stationary stereo camera technology, and the second contribution is the fusion of monocular and stereo camera technologies. A vision-based ITS consists of several hardware and software components. The overall performance of such a system does not only depend on these single modules but also on their interaction. Therefore, a systematic approach considering all essential modules was chosen instead of focusing on one element of the complete system chain. This leads to detailed investigations of several core algorithms, e.g. background subtraction, histogram based fingerprints, and data fusion methods. From experimental results on standard datasets, we concluded that proposed fusion-based approach, consisting of monocular and stereo camera technologies performs better than each particular technology for vehicle detection and vehicle recognition. Moreover, this research work has a potential to provide a low-cost vision-based solution for online traffic monitoring systems in urban and rural environments.