Moving object detection for vehicle tracking in Wide Area Motion Imagery using 4D filtering

Palaniappan, Kannappan; Poostchi, Mahdieh; Aliakbarpour, Hadi; Viguier, Raphael; Fraser, Joshua; Bunyak, Filiz; Basharat, Arslan; Suddarth, Steven C.; Blasch, Erik; Rao, Raghuveer M.; Seetharaman, Guna

doi:10.1109/icpr.2016.7900065

Cited by 36 publications

(7 citation statements)

References 19 publications

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“…The challenges for national include the data types, the analytical process, the derived explanation, and the dissemination. For example, large amounts of physical-based data coming from full motion video [26,27], wide-area motion imagery [28,29,30], infrared devices [31], and radar [32,33,34], and human-derived data (e.g., text) complicates the uniformity of the standard type and compliance. Furthermore, cultural considerations from developed models based on human, social, cultural, and behavior attributes is difficult to derive and utilize [35].…”

Section: Motivationmentioning

confidence: 99%

Multisource AI scorecard table analysis of AMIGO

Blasch

Shen

Chen

et al. 2021

Sensors and Systems for Space Applications XIV

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The paper describes a Multisource AI Scorecard Table (MAST) that provides the developer and user of an artificial intelligence (AI)/machine learning (ML) system with a standard checklist focused on the principles of good analysis adopted by the intelligence community (IC) to help promote the development of more understandable systems and engender trust in AI outputs. Such a scorecard enables a transparent, consistent, and meaningful understanding of AI tools applied for commercial and government use. A standard is built on compliance and agreement through policy, which requires buy-in from the stakeholders. While consistency for testing might only exist across a standard data set, the community requires discussion on verification and validation approaches which can lead to interpretability, explainability, and proper use. The paper explores how the analytic tradecraft standards outlined in Intelligence Community Directive (ICD) 203 can provide a framework for assessing the performance of an AI system supporting various operational needs. These include sourcing, uncertainty, consistency, accuracy, and visualization. Three use cases are presented as notional examples that support security for comparative analysis.

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Section: Motivationmentioning

confidence: 99%

Multisource AI scorecard table analysis of AMIGO

Blasch

Shen

Chen

et al. 2021

Sensors and Systems for Space Applications XIV

Self Cite

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“…In many video analytics frameworks feature likelihood maps represented as histograms play a critical role in the overall algorithm. Histogram-based regional descriptors are fundamental building blocks of many multimedia analysis tasks including filtering [1], [2], [3], classification and recognition [4], [5], video content retrieval [6], [7], [8], object detection [9], [10], visual tracking [11], [12], [13], 3D reconstruction [14], [15] and video surveillance systems [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Fast Integral Histogram Computations on GPU for Real-Time Video Analytics

Poostchi¹,

Palaniappan²,

Li³

et al. 2017

Preprint

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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 × 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.

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“…Furthermore if detection relies on motion, even after registration the parallax can cause a very high number of false alarms unless additional filtering measures are taken. For that purpose, Altitude maps generated from the 3D data can prove to be very useful by filtering out impossible locations for a vehicle [78,79].…”

Section: Altitude Masksmentioning

confidence: 99%

3D city scale reconstruction using wide area motion imagery

Viguier¹

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3D reconstruction is one of the most challenging but also most necessary part of computer vision. It is generally applied everywhere, from remote sensing to medical imaging and multimedia. Wide Area Motion Imagery is a field that has gained traction over the recent years. It consists in using an airborne large field of view sensor to cover a typically over a square kilometer area for each captured image. This is particularly valuable data for analysis but the amount of information is overwhelming for any human analyst. Algorithms to efficiently and automatically extract information are therefore needed and 3D reconstruction plays a critical part in it, along with detection and tracking. This dissertation work presents novel reconstruction algorithms to compute a 3D probabilistic space, a set of experiments to efficiently extract photo realistic 3D point clouds and a range of transformations for possible applications of the generated 3D data to filtering, data compression and mapping. The algorithms have been successfully tested on our own datasets provided by Transparent Sky and this thesis work also proposes methods to evaluate accuracy, completeness and photo-consistency. The generated data has been successfully used to improve detection and tracking performances, and allows data compression and extrapolation by generating synthetic images from new point of view, and data augmentation with the inferred occlusion areas.

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Moving object detection for vehicle tracking in Wide Area Motion Imagery using 4D filtering

Cited by 36 publications

References 19 publications

Multisource AI scorecard table analysis of AMIGO

Multisource AI scorecard table analysis of AMIGO

Fast Integral Histogram Computations on GPU for Real-Time Video Analytics

3D city scale reconstruction using wide area motion imagery

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