Smart surveillance framework: A versatile tool for video analysis

Abstract

Computer Vision problems applied to visual surveillance have been studied for several years aiming at finding accurate and efficient solutions, required to allow the execution of surveillance systems in real environments. The main goal of such systems is to analyze the scene focusing on the detection and recognition of suspicious activities performed by humans in the scene, so that the security personnel can pay closer attention to these preselected activities. To accomplish that, several problems have to be solved first, for instance background subtraction, person detection, tracking and re-identification, face recognition, and action recognition. Even though each of these problems have been researched in the past decades, they are hardly considered in a sequence, each one is usually solved individually. However, in a real surveillance scenarios, the aforementioned problems have to be solved in sequence considering only videos as the input. Aiming at the direction of evaluating approaches in more realistic scenarios, this work proposes a framework called Smart Surveillance Framework (SSF), to allow researchers to implement their solutions to the above problems as a sequence of processing modules that communicate through a shared memory.

Benchmark for license plate character segmentation

“…The segmentation and OCR approaches were implemented on the Smart Surveillance Framework 42 using OpenCV and C/C++ programming language. All experiments were performed on a computer with a Intel Xeon E5-2620, 32GB of RAM and a dedicated 100 GB hard drive for storage.…”

Automatic surveillance in transportation hubs: No longer just about catching the bad guy

“…Li, Wu, Karanam, and Radke, 2014 considers person re-detection in an airport environment; while Arroyo, Yebes, Bergasa, Daza, and Almazán, 2015 consider suspicious behaviour detection in a shopping mall). Similarly, when considering the system wide implications of video surveillance, the literature has focussed on aspects such as the data and networking requirements of such large scale systems (Ajiboye, Birch, Chatwin, & Young, 2015;Chang, Wang, Wang, Liu, & Ho, 2012); interfaces to retrieve and display results (Ye, Liao, Dong, Zeng, & Zhong, 2015); or the needs of researchers and developers to aid in the development of such techniques (Nazare, dos Santos, Ferreira, & Robson Schwartz, 2014).…”

Cognitive surveillance architecture for scenario understanding

“…One decade later from the first approach in this field, authors are still looking for novel frameworks to allow further development on computer vision methods and surveillance applications [Nazare14][Sobhani15]. However, while the Smart Surveillance Framework proposed in [Nazare14] innovates with the introduction of a complex query server that facilitates the scene understanding, authors in [Sobhani15] focused only on ontological knowledge description as a vital requirement at the earliest stage of development of an advanced intelligent forensic retrieval system.…”