Intensity and resolution enhancement of local regions for object detection and tracking in wide area surveillance

2015 ◽  
Author(s):  
Evan Krieger ◽  
Vijayan K. Asari ◽  
Saibabu Arigela ◽  
Theus Aspiras
Keyword(s):  
Object Detection ◽  
Resolution Enhancement ◽  
Wide Area ◽  
Object Detection And Tracking ◽  
Detection And Tracking ◽  
Area Surveillance ◽  
Wide Area Surveillance

scholarly journals Robust Small-Object Detection for Outdoor Wide-Area Surveillance

2008 ◽  
Vol E91-D (7) ◽  
pp. 1922-1928 ◽  
Author(s):  
D. ABE ◽  
E. SEGAWA ◽  
O. NAKAYAMA ◽  
M. SHIOHARA ◽  
S. SASAKI ◽  
...  
Keyword(s):  
Object Detection ◽  
Wide Area ◽  
Small Object ◽  
Area Surveillance ◽  
Small Object Detection ◽  
Wide Area Surveillance

Efficient Key Frame Selection Approach for Object Detection in Wide Area Surveillance Applications

2019 ◽  
pp. 609-623
Author(s):  
Almabrok Essa ◽  
Paheding Sidike ◽  
Vijayan K. Asari
Keyword(s):  
Object Detection ◽  
Wide Area ◽  
Surveillance Video ◽  
Performance Impact ◽  
Frame Selection ◽  
Key Frame ◽  
Key Frame Selection ◽  
Area Surveillance ◽  
Surveillance Applications ◽  
Wide Area Surveillance

This paper presents an efficient preprocessing algorithm for object detection in wide area surveillance video analysis. The proposed key-frame selection method utilizes the pixel intensity differences among subsequent frames to automatically select only the frames that contain the desired contextual information and discard the rest of the insignificant frames. For improving effectiveness and efficiency, a batch updating based on a modular representation strategy is also incorporated. Experimental results show that the proposed key frame selection technique has a significant positive performance impact on wide area surveillance applications such as automatic object detection and recognition in aerial imagery.

Efficient Key Frame Selection Approach for Object Detection in Wide Area Surveillance Applications

2015 ◽  
Vol 3 (2) ◽  
pp. 20-34
Author(s):  
Almabrok Essa ◽  
Paheding Sidike ◽  
Vijayan K. Asari
Keyword(s):  
Object Detection ◽  
Wide Area ◽  
Surveillance Video ◽  
Pixel Intensity ◽  
Frame Selection ◽  
Key Frame ◽  
Key Frame Selection ◽  
Area Surveillance ◽  
Surveillance Applications ◽  
Wide Area Surveillance

This paper presents an efficient preprocessing algorithm for object detection in wide area surveillance video analysis. The proposed key-frame selection method utilizes the pixel intensity differences among subsequent frames to automatically select only the frames that contain the desired contextual information and discard the rest of the insignificant frames. For improving effectiveness and efficiency, a batch updating based on a modular representation strategy is also incorporated. Experimental results show that the proposed key frame selection technique has a significant positive performance impact on wide area surveillance applications such as automatic object detection and recognition in aerial imagery.

scholarly journals The INUS Platform: A Modular Solution for Object Detection and Tracking from UAVs and Terrestrial Surveillance Assets

Computation ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 12
Author(s):  
Evangelos Maltezos ◽  
Athanasios Douklias ◽  
Aris Dadoukis ◽  
Fay Misichroni ◽  
Lazaros Karagiannidis ◽  
...  
Keyword(s):  
Decision Making ◽  
Object Detection ◽  
Situational Awareness ◽  
Machine Learning Techniques ◽  
Measurement Instruments ◽  
Time Data ◽  
Modular Architecture ◽  
Seamless Integration ◽  
Object Detection And Tracking ◽  
Detection And Tracking

Situational awareness is a critical aspect of the decision-making process in emergency response and civil protection and requires the availability of up-to-date information on the current situation. In this context, the related research should not only encompass developing innovative single solutions for (real-time) data collection, but also on the aspect of transforming data into information so that the latter can be considered as a basis for action and decision making. Unmanned systems (UxV) as data acquisition platforms and autonomous or semi-autonomous measurement instruments have become attractive for many applications in emergency operations. This paper proposes a multipurpose situational awareness platform by exploiting advanced on-board processing capabilities and efficient computer vision, image processing, and machine learning techniques. The main pillars of the proposed platform are: (1) a modular architecture that exploits unmanned aerial vehicle (UAV) and terrestrial assets; (2) deployment of on-board data capturing and processing; (3) provision of geolocalized object detection and tracking events; and (4) a user-friendly operational interface for standalone deployment and seamless integration with external systems. Experimental results are provided using RGB and thermal video datasets and applying novel object detection and tracking algorithms. The results show the utility and the potential of the proposed platform, and future directions for extension and optimization are presented.

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