Search

Detection of moving objects such as vehicles in videos acquired from an airborne camera is very useful for video analytics applications. Using fast low power algorithms for onboard moving object detection would also provide region of interest-based semantic information for scene content aware image compression. This would enable more efficient and flexible communication link utilization in lowbandwidth airborne cloud computing networks. Despite recent advances in both UAV or drone platforms and imaging sensor technologies, vehicle detection from aerial video remains challenging due to small object sizes, platform motion and camera jitter, obscurations, scene complexity and degraded imaging conditions. This paper proposes an efficient moving vehicle detection pipeline which synergistically fuses both appearance and motion-based detections in a complementary manner using deep learning combined with flux tensor spatio-temporal filtering. Our proposed multi-cue pipeline is able to detect moving vehicles with high precision and recall, while filtering out false positives such as parked vehicles, through intelligent fusion. Experimental results show that incorporating contextual information of moving vehicles enables high semantic compression ratios of over 100:1 with high image fidelity, for better utilization of limited bandwidth air-to-ground network links., Comment: CVPR2019 Workshop on Detecting Objects in Aerial Images

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 the computation compared to a strip-based organization. The tiled integral histogram using a diagonal wavefront scan has the best performance of about 300.4 frames/sec for 640 x 480 images and 32 bins with a speedup factor of about 120 using GTX Titan X graphics card compared to a single threaded sequential CPU implementation. Double-buffering has been exploited to overlap computation and communication across sequence of images. Mapping integral histogram bins computations onto multiple GPUs enables us to process 32 giga bytes integral histogram data (of 64MB Image and 128 bins) with a frame rate of 0.73 Hz and speedup factor of 153X over single-threaded CPU implementation and the speedup of 45X over 16-threaded CPU implementation., Comment: GPU Implementation of Integral Histogram

Weighting pixel contribution considering its location is a key feature in many fundamental image processing tasks including filtering, object modeling and distance matching. Several techniques have been proposed that incorporate Spatial information to increase the accuracy and boost the performance of detection, tracking and recognition systems at the cost of speed. But, it is still not clear how to efficiently ex- tract weighted local histograms in constant time using integral histogram. This paper presents a novel algorithm to compute accurately multi-scale Spatially weighted local histograms in constant time using Weighted Integral Histogram (SWIH) for fast search. We applied our spatially weighted integral histogram approach for fast tracking and obtained more accurate and robust target localization result in comparison with using plain histogram., Comment: 5 pages, 7 figures

A dynamic programming based approach for data compression of a ID sequence is presented. The compression of an input sequence of size N to that of a smaller size k is achieved by dividing the input sequence into k subsequences and replacing the subsequences by their respective average values. The partitioning of the input sequence is carried with the intention of reducing the mean squared error in the reconstructed sequence. The complexity involved in finding the partitions which would result in such an optimal compressed sequence is reduced by using the dynamic programming approach, which is presented.