Visual object tracking is an active and challenging computer vision research domain having wide range of civil and defence applications. Mean shift (MS) is a commonly used target tracking technique due to its ease of implementation and real time response. However, it has certain short-comings that limits its tracking performance. In this thesis short comings of MS tracking like poor localization, complicated background distraction, partial/ full occlusion and distraction due to similar target resemblance are addressed using 2D and 3D features. To improve MS target localization problem due to the presence of complex/mingled background features (in target representation), a novel 2D spatio-spectral technique is proposed. True background weighted histogram features are identified in target model representation using spectral and spatial weighting. A transformation is then applied to minimize their effect in target model representation for localization improvement. Edge based centroid re-positioning is applied to adjust/re-position the MS estimated target position for further localization improvement. Occlusion avoidance method is developed for MS tracking algorithm using adaptive window normalized cross correlation (NCC) based template matching. The Bhattacharyya coefficient based similarity threshold is used to detect partial/full occlusion and to initiate the NCC part in MS tracking. A target model updation for background weighted histogram through online feature consistency data is also proposed. The proposed 2D MS tracking techniques effectively solved the tracking problems of clutter, similar target resemblance, complex/fast object movement and partial/ full occlusion. The failure cases for proposed 2D tracking technique include guidewire tip tracking for image guided cardiovascular interventions. The guidewire tip being thin, featureless and deformable structure is easily distracted with its own and similar object like vane iii structures in neighborhood. Moreover, the tracking of guidewire under low contrast fluoroscopic images and abrupt shape variations due to cardiac motion make the problem more challenging. 3D visual tracking techniques are used to incorporate object depth information to improve robustness. However, the existing 3D tracking techniques lack accuracy and robustness mainly due to non availability of precise depth features. In this thesis, depth features are acquired through shape from focus (SFF) technique and integrated with spectral and spatial features for robust 3D target representation/tracking. For 3D shape representation through SFF, a novel adaptive focus measure based on linear combination of multiple morphological gradient operators is proposed. The morphological edge gradient operators aided by multi-structuring elements are employed for sharpness measurement. The robust focus measure is then computed by combining the weighted response of gradient operators. The depth features acquired are integrated in joint histogram with grey level intensity and texture features to develop a novel technique for real time 3D representation and tracking of guidewire for image guided cardiovascular interventions. The grey level intensity is represented through conventional histogram method whereas the texture and depth features are represented through filtered local binary pattern histogram and filtered local depth pattern histogram respectively. The result shows the significant improvement in the accuracy, robustness and computational efficiency through proposed 2D/3D MS tracking and depth estimation techniques.