Spatial, Temporal, and Interchannel Image Data Fusion       for Long‐Distance Terrestrial Observation Systems

Fishbain, Barak; Yaroslavsky, Leonid P.; Ideses, Ianir A.

doi:10.1155/2008/546808

Cited by 10 publications

(3 citation statements)

References 57 publications

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“…This behavior is common when a shooting occurs. We then apply spatial aggregation to the resulting temporal aggregation by considering neighboring cells given by equation (6), which counts the number of neighboring cells that exceed a specific threshold TR. The resulting grid is spatially and temporally aggregated.…”

Section: Methodsmentioning

confidence: 99%

“…Some studies suggest the utilization of both audio and video input for event detection and indexing [1]. Other work extends these ideas to include features extracted from the video itself, such as text [2] while some studies suggest the indexing and integration of multiple video streams: these can be sourced from the same modality camera, (e.g., visible light cameras) [3], [4] or from cameras of different modalities (e.g., infrared and visible light) [5], [6]. A comprehensive review of these methods in the context of surveillance can be found in [7]: importantly all these studies utilize data that are extracted solely from video sources, i.e., none of these studies explicitly address the task of combining sources of different modalities for event detection.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Janus - Multi Source Event Detection and Collection System for Effective Surveillance of Criminal Activity

Shahabi¹,

Kim²,

Nocera³

et al. 2014

Journal of Information Processing Systems

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Recent technological advances provide the opportunity to use large amounts of multimedia data from a multitude of sensors with different modalities (e.g., video, text) for the detection and characterization of criminal activity. Their integration can compensate for sensor and modality deficiencies by using data from other available sensors and modalities. However, building such an integrated system at the scale of neighborhood and cities is challenging due to the large amount of data to be considered and the need to ensure a short response time to potential criminal activity. In this paper, we present a system that enables multi-modal data collection at scale and automates the detection of events of interest for the surveillance and reconnaissance of criminal activity. The proposed system showcases novel analytical tools that fuse multimedia data streams to automatically detect and identify specific criminal events and activities. More specifically, the system detects and analyzes series of incidents (an incident is an occurrence or artifact relevant to a criminal activity extracted from a single media stream) in the spatiotemporal domain to extract events (actual instances of criminal events) while cross-referencing multimodal media streams and incidents in time and space to provide a comprehensive view to a human operator while avoiding information overload. We present several case studies that demonstrate how the proposed system can provide law enforcement personnel with forensic and real time tools to identify and track potential criminal activity.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Janus - Multi Source Event Detection and Collection System for Effective Surveillance of Criminal Activity

Shahabi¹,

Kim²,

Nocera³

et al. 2014

Journal of Information Processing Systems

View full text Add to dashboard Cite

show abstract

“…Elastic image registration is proposed by J Kybic et al that incorporates geometric mapping that is locally affine and also globally smooths [11]. Elastic image registration technique can also be used in medical imaging is proposed by S. Periaswamy et al [12]. The CLEAR algorithm proposed by N Anantrasirichai et al [1] [13], stabilizes the video by aligning (by translation) the centers of corresponding regions of interest (ROI).…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Image Registration Technique to Remove Atmospheric Turbulence

Patel¹,

Israni

Kumar³

et al. 2019

Stat., optim. inf. comput.

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Turbulence/Heat Scintillations is the change that arises in the refraction index of air with the temperature. The distortion created by the atmospheric turbulence is proportional to the distance between object and camera. In last few years, several approaches have been proposed to estimate and eliminate geometric distortion and blur. In this paper, a novel technique is proposed that improves the visual quality of video sequences affected by atmospheric turbulence. The proposed method is based on adaptive control grid interpolation (CGI). CGI approximates accurate motion vectors to generate a geometrically correct frame using certain reference frames. For high scintillation sequences, CGI doesn ′ t mitigate scintillations completely. The new methodology is proposed with updated trajectory estimation. The proposed method can effectively reduce the influence even for high atmospheric turbulence. Experimental results also prove that proposed approach is time efficient compare to traditional CGI.

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