Object tracking from videos is still a challenging task due to various changes throughout a video sequence including occlusions, motion blur, scale and other deformation changes. In this paper, we propose a selective parts-based approach, using correlation filters, that makes choices based on a consensus of the parts and global tracking. Moreover, we further enhance our parts-based approach by introducing a segmentation-assisted parts initialization. In addition, we present a genetic algorithmbased method to autonomously select various parameters of the tracking algorithm, as opposed to the common practice of manually tuning those parameters. In contrast to existing partbased methods, the proposed method does not dilute accurate tracking by averaging results over multiple parts at every frame. Instead, we take a selective approach based on the relative weight of the responses across parts. Moreover, we only make location corrections when a part diverges, and rely on these location corrections to maintain an accurate appearance model. In the case of occlusions, which are among the main reasons for using a parts-based approach, our proposed approach consistently achieves the best performance. It is due to the ability to handle occlusion and not dilute decisions with incorrect parts, that our proposed approach enables state-of-the-art performance. The proposed approach was evaluated on videos from three different challenging benchmark datasets. Our approach has resulted in better overall precision and success rates for three different base tracking approaches.

Multiple model estimation is an important problem in computer vision. Through estimation, one can detect important structural information in an image. A crucial step in multiple model estimation is the ability to dichotomize inliers of a model from outliers. This paper proposes a novel technique for estimating the scale of a model. In contrast to previous adaptive scale estimate works, our method removes the need for user provided input. We achieve accurate scale estimation through consecutive inspection of the ordered residuals. Our results show the ability of the proposed scale estimate metric to maintain accurate scale estimation even with over 90% outliers present in the data. Likewise, we also apply our scale estimator with multiple model estimation problems for detecting planes and two-view motions, demonstrating the ability of our approach to accurately estimate scale in real application oriented scenarios.

The objective of this work is to detect a class of objects in images or video using multi-scale voting with random Hough Forests. Hough Forests have several nice properties, including that an implicit shape model is automatically learned from cropped images of a particular class of object and the voting induced by a Hough technique allows the detection method to handle partial occlusions. Typical Hough Forest voting is however scale sensitive when it comes to both training and testing. Currently, searching for multiple scales for an object size is achieved by re-running the detection routine for a given image at numerous manually provided input scales. This work will demonstrate that manually input scale parameters can lower detection rates if all scales in the test set are not accounted for. The novelty of our proposed work is in the creation of an autonomous scale estimation and multi-scalar detection Hough Forest voting technique. The technique proposed to accomplish the automatic scale estimation is to view votes as not votes for discrete locations, but rather as voting rays. The intersection of these rays can then be used to automatically determine the estimated object's center and scale.

The objective of this paper is to group feature points on different planes as a means of semantic image segmentation and understanding. The methodology is based on the ability to estimate planar homographies from grouped feature points spanning different unknown number of planes. This paper proposes an alternative to the J-linkage method, which was shown to have benefits in terms of accuracy over other multiple model estimation techniques. J-linkage is an agglomerative clustering technique that uses a set representation of support for a set of possible planar homographies and the Jaccard measure to determine the distance between support sets. The technique proposed in this paper uses a frequency vector to represent the support for a model. This formulation promotes clustering even in the presence of noise and prevents the order in which agglomerative clustering is performed from influencing the results. The feature vector representation requires an alternative distance measure to Jaccard to be exercised, that of cosine similarity. Hence, the method proposed here is called C-linkage. The results show that, compared to the J-linkage method, the proposed technique correctly classifies more points on each plane, and results in less over-segmentation while providing higher Normalized Mutual Information scores for a range of multiple model estimation problems on different datasets.