Abstract

Video data is increasing rapidly along with the capacity of storage devices owned by a lay user. Users have mod­erate to large personal collections of videos and would like to keep them in an organized manner based on its content. Video organizing tools for personal users are way behind even the primitive image organizing tools. We present a mechanism in this paper to help ordinary users organize their personal collection of videos based on categories they choose. We cluster the PHOG features extracted from se­lected key frames to form a representation for each user­selected category during the learning phase. During the organization phase, labels from a K -NN classifier on these cluster centres for each key frame are aggregated to give a label to the video while categorizing. Video processing is computationally intensive. To peiform the computationally intensive steps involved, we exploit the CPU as well as the GPU that is common even on personal systems. Effective use of the parallel hardware on the system is the only way to make the tool scale reasonably to large collections that will be available soon. Our tool is able to organize a set of 100 sport videos of total duration of 1375 minutes in about 9.5 minutes. The process of learning the categories from 12 annotated videos of duration 165 minutes took 75 seconds on a GTX 580 card. These were on a standard desktop with an off-the-shelfGPU. The labeling accuracy is about 96% on all video

Abstract

We propose an object centric representation for easy and in-tuitive navigation and manipulation of videos. Object centricrepresentation allows a user to directly access and process ob-jects as basic video components. We demonstrate a trajectorybased interface and example operations, which allow users toretime, reorder, remove or clone video objects in a ‘click anddrag’ fashion. This interface is created by extracting objectmotion information from the video. We use object detectionand tracking to obtain spatiotemporal video object tube. Thecorresponding object motion trajectories are represented in a3D(x,y,t)grid. Users can navigate and manipulate videoobjects by scrubbing or manipulating corresponding trajecto-ries. We show some example applications of proposed inter-face like object synchronization, saliency magnification, vi-sual effects and composite video creation.

Abstract

Raytracing dynamic scenes at interactive rates have received a lot of attention recently. We present a few strategies forhigh performance raytracing on a commodity GPU. The construction of grids needs sorting, which is fast on today’s GPUs. The grid isthus the acceleration structure of choice for dynamic scenes as per-frame rebuilding is required. We advocate the use of appropriatedata structures for each stage of raytracing, resulting in multiple structure building per frame. A perspective grid built for the cameraachieves perfect coherence for primary rays. A perspective grid built with respect to each light source provides the best performancefor shadow rays. Spherical grids handle lights positioned inside the model space and handle spotlights. Uniform grids are best forreflection and refraction rays with little coherence. We propose an Enforced Coherence method to bring coherence to them byrearranging the ray to voxel mapping using sorting. This gives the best performance on GPUs with only user-managed caches. Wealso propose a simple, Independent Voxel Walk method, which performs best by taking advantage of the L1 and L2 caches on recentGPUs. We achieve over 10 fps of total rendering on the Conference model with one light source and one reflection bounce, whilerebuilding the data structure for each stage. Ideas presented here are likely to give high performance on the future GPUs as well asother manycore architectures

Abstract

Advances in cameras and web technology have madeit easy to capture and share large amounts of video data over toa large number of people. A large number of cameras overseepublic and semi-public spaces today. These raise concerns on theunintentional and unwarranted invasion of the privacy of individ-uals caught in the videos. To address these concerns, automatedmethods tode-identifyindividuals in these videos are necessary.De-identification does not aim at destroying all informationinvolving the individuals. Its ideal goals are to obscure the identityof the actor without obscuring the action. This paper outlinesthe scenarios in which de-identification is required and the issuesbrought out by those. We also present an approach to de-identifyindividuals from videos. Our approach involves tracking andsegmenting individuals in a conservative voxel space involvingx, y,and time. A de-identification transformation is applied perframe using these voxels to obscure the identity. Face, silhouette,gait, and other characteristics need to be obscured, ideally. Weshow results of our scheme on a number of videos and forseveral variations of the transformations. We present the resultsof applying algorithmic identification on the transformed videos.We also present the results of a user-study to evaluate how wellhumans can identify individuals from the transformed videos

Abstract

Graphics processing units provide a large computational power at a very low price which position them as an ubiquitous accelerator. Efficient primitives that can expand the r ange of operations performed on the GPU are thus important. Discrete Range Searching(DRS) is one such primitive with direct applications to string processing, document and text retrieval systems, and least common ancestor queries. In this work, we present a GPU specific implementation of DRS with an optimal space-time trade off. Toward this end, we also present GPU amenable succinct representations and discuss limitations on the GPU. Our method uses 7.5 bits of additional space per element. The speedup achieved by our method is in the range of 20-25 for preprocessing, and 25-35 for batch querying over a sequential implementation. Compared to an 8-threaded implementation, our methods obtain a speedup of 6-8. We study applications of the DRS on the GPU. Also, we suggest that most graph algorithms which focus on using least common ancestor, can easily be enabled on the GPU based on range minima primitive. Beyond this, we show applications of DRS in string querying and tree queries, and suggest how DRS can be helpful in implementing tree based graph algorithms on the GPU.

Abstract

Graphics Processing Units (GPU) are application specific accelerators which provide high performance to cost ratio and are widely available and used, hence places them as a ubiquitous accelerator. A computing paradigm based on the same is the general purpose computing on the GPU (GPGPU) model. The GPU due to its graphics lineage is better suited for the data-parallel, data-regular algorithms. The hardware architecture of the GPU is not suitable for the data parallel but data irregular algorithms such as graph connected components and list ranking. In this paper, we present results that show how to use GPUs efficiently for graph algorithms which are known to have irregular data access patterns. We consider two fundamental graph problems: finding the connected components and finding a spanning tree. These two problems find applications in several graph theoretical problems. In this paper we arrive at efficient GPU implementations for the above two problems. The algorithms focus on minimising irregularity at both algorithmic and implementation level. Our implementation achieves a speedup of 11-16 times over a corresponding best sequential implementation.

Abstract

Graphics processing units provide a large compu-tational power at a very low price which position them as an ubiquitous accelerator. General purpose programming on the graphics processing units (GPGPU) is best suited for regular data parallel algorithms. They are not directly amenable for algorithms which have irregular data access patterns such as list ranking, and finding the connected components of a graph, and the like. In this work, we present a GPU-optimized implementation for finding the connected components of a given graph. Our implementation tries to minimize the impact of irregularity, both at the data level and functional level. Our implementation achieves a speed up of 9 to 12 times over the best sequential CPU implementation. For instance, our implementation finds connected components of a graph of 10 million nodes and 60 million edges in about 500 milliseconds on a GPU, given a random edge list. We also draw interesting observations on why PRAM algorithms, such as the Shiloach-Vishkin algorithm may not be a good fit for the GPU and how they should be modified.

Abstract

Large-scale 3D reconstruction has received a lot of attentionrecently. Bundle adjustment is a key component of the reconstructionpipeline and often its slowest and most computational resource intensive.It hasn’t been parallelized effectively so far. In this paper, we present ahybrid implementation of sparse bundle adjustment on the GPU usingCUDA, with the CPU working in parallel. The algorithm is decomposedinto smaller steps, each of which is scheduled on the GPU or the CPU. Wedevelop efficient kernels for the steps and make use of existing libraries forseveral steps. Our implementation outperforms the CPU implementationsignificantly, achieving a speedup of 30-40 times over the standard CPUimplementation for datasets with upto 500 images on an Nvidia TeslaC2050 GPU.

Abstract

Genetic algorithms are effective in solving many optimiza-tion tasks. However, the long execution time associated withit prevents its use in many domains. In this paper, we pro-pose a new approach for parallel implementation of geneticalgorithm on graphics processing units (GPUs) using CUDAprogramming model. We exploit the parallelism within achromosome in addition to the parallelism across multiplechromosomes. The use of one thread per chromosome byprevious efforts does not utilize the GPU resources effec-tively. Our approach uses multiple threads per chromosome,thereby exploiting the massively multithreaded GPU moreeffectively. This results in good utilization of GPU resourceseven at small population sizes while maintaining impressivespeed up for large population sizes. Our approach is mod-eled after the GAlib library and is adaptable to a varietyof problems. We obtain a speedup of over 1500 over theCPU on problems involving a million chromosomes. Prob-lems of such magnitude are not ordinarily attempted due tothe prohibitive computation times.

Abstract

Graphics processing units provide a large compu-tational power at a very low price which position them as anubiquitous accelerator. General purpose programming on thegraphics processing units (GPGPU) is best suited for regulardata parallel algorithms. They are not directly amenable foralgorithms which have irregular data access patterns suchas list ranking, and finding the connected components of agraph, and the like. In this work, we present a GPU-optimizedimplementation for finding the connected components of agiven graph. Our implementation tries to minimize the impactof irregularity, both at the data level and functional level.Our implementation achieves a speed up of 9 to 12 timesover the best sequential CPU implementation. For instance, ourimplementation finds connected components of a graph of 10million nodes and 60 million edges in about 500 millisecondson a GPU, given a random edge list. We also draw interestingobservations on why PRAM algorithms, such as the Shiloach-Vishkin algorithm may not be a good fit for the GPU and howthey should be modified.