This paper presents a novel locality sensitive histogram (LSH) algorithm for visual tracking. Unlike the conventional image histogram that counts the frequency of occurrence of each intensity value by adding ones to the corresponding bin, an LSH is computed at each pixel location, and a floating-point value is added to the corresponding bin for each occurrence of an intensity value. The floating-point value exponentially reduces with respect to the distance to the pixel location where the histogram is computed. An efficient algorithm is proposed that enables the LSHs to be computed in time linear in the image size and the number of bins. In addition, this efficient algorithm can be extended to exploit color images. A robust tracking framework based on the LSHs is proposed, which consists of two main components: a new feature for tracking that is robust to illumination change and a novel multiregion tracking algorithm that runs in real time even with hundreds of regions. Extensive experiments demonstrate that the proposed tracking framework outperforms the state-of-the-art methods in challenging scenarios, especially when the illumination changes dramatically. Evaluation using the latest benchmark shows that our algorithm is the top performer.

We present a method to build 3D structured mechanical collages consisting of numerous elements from the database given artist-designed proxy models. The construction is guided by some graphic design principles, namely unity, variety and contrast. Our results are visually more pleasing than previous works as confirmed by a user study.

This paper presents a novel locality sensitive histogram algorithm for visual tracking. Unlike the conventional image histogram that counts the frequency of occurrences of each intensity value by adding ones to the corresponding bin, a locality sensitive histogram is computed at each pixel location and a floating-point value is added to the corresponding bin for each occurrence of an intensity value. The floating-point value declines exponentially with respect to the distance to the pixel location where the histogram is computed, thus every pixel is considered but those that are far away can be neglected due to the very small weights assigned. An efficient algorithm is proposed that enables the locality sensitive histograms to be computed in time linear in the image size and the number of bins. A robust tracking framework based on the locality sensitive histograms is proposed, which consists of two main components: a new feature for tracking that is robust to illumination changes and a novel multi-region tracking algorithm that runs in real time even with hundreds of regions. Extensive experiments demonstrate that the proposed tracking framework outperforms the state-of-the-art methods in challenging scenarios, especially when the illumination changes dramatically.

Due to the popularity of motion capture data in many applications, such as games, movies and virtual environments, huge collections of motion capture data are now available. It is becoming important to store these data in compressed form while being able to retrieve them without much overhead. However, there is little work that addresses both issues together. In this paper, we address these two issues by proposing a novel database architecture. First, we propose a lossless compression algorithm to compress the motion clips, which is based on a novel Alpha Parallelogram Predictor (APP) to estimate the degree of freedom (DOF) of each child joint from its immediate neighbors and parents that have already been processed. Second, we propose to store selected eigenvalues and eigenvectors of each motion clip, which only require a very small amount of memory overheads, for faster filtering of irrelevant motions. Based on this architecture, real-time queries become a three-step process. In the first two steps, we perform a quick filtering to identify relevant motion clips in the database through a two-level indexing structure. In the third step, only a small number of candidate clips are uncompressed and accurately matched with a Dynamic Time Warping algorithm. Our results show that users can efficiently search clips from this losslessly compressed motion database.