Fast Integral Histogram Computations on GPU for Real-Time Video Analytics
Mahdieh Poostchi, Kannappan Palaniappan, Da Li, Michela Becchi, Filiz, Bunyak, Guna Seetharaman

TL;DR
This paper presents optimized GPU algorithms for fast integral histogram computation, enabling real-time video analytics with significant speedups over CPU implementations, including multi-GPU scaling.
Contribution
It introduces efficient GPU kernel strategies, tiling, and multi-GPU mapping for integral histograms, achieving high frame rates and large data processing capabilities.
Findings
Achieved 300 frames/sec for 640x480 images with 32 bins on a single GPU.
Realized 0.73 Hz processing of 32 GB data using multiple GPUs.
Speedup factors of up to 153x over single-threaded CPU implementations.
Abstract
In many Multimedia content analytics frameworks feature likelihood maps represented as histograms play a critical role in the overall algorithm. Integral histograms provide an efficient computational framework for extracting multi-scale histogram-based regional descriptors in constant time which are considered as the principle building blocks of many video content analytics frameworks. We evaluate four different mappings of the integral histogram computation onto Graphics Processing Units (GPUs) using different kernel optimization strategies. Our kernels perform cumulative sums on row and column histograms in a cross-weave or wavefront scan order, use different data organization and scheduling methods that is shown to critically affect utilization of GPU resources (cores and shared memory). Tiling the 3-D array into smaller regular data blocks significantly speeds up the efficiency of…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Advanced Vision and Imaging · Video Analysis and Summarization
