Despite the continued successes of computationally efficient deep neural network architectures for video object detection, performance continually arrives at great trilemma speed versus accuracy computational resources (pick two). Current attempts to exploit temporal information in data overcome this are bottlenecked by state art detection models. This work presents motion vector extrapolation ...

Intelligent video surveillance systems detect pre-configured surveillance events through background modeling, foreground and object extraction, object tracking, and event detection. Shadow regions inside video frames sometimes appear as foreground objects, interfere with ensuing processes, and finally degrade the event detection performance of the systems. Conventional studies have mostly used ...

The object detection in video is one of the critical task. In this paper, a hybrid model is presented to perform the segmentation over the video. The work includes the detection of video frame that is followed by series of image processing operations to detect the object. In this work, a watershed improved morphological approach is defined to perform the segmentation. This segmentation process ...

Convolutional neural networks (CNN) currently dominate the computer vision landscape. Recently, a CNN based model, Faster R-CNN [1], achieved stateof-the-art performance at object detection on the PASCAL VOC 2007 and 2012 datasets. It combines region proposal generation with object detection on a single frame in less than 200ms. We apply the Faster R-CNN model to video clips from the ImageNet 2...

Fusion of three-dimensional data from multiple sensors gained momentum, especially in applications pertaining to surveillance, when promising results were obtained in moving object detection. Several approaches to video fusion of visual and infrared data have been proposed in recent literature. They mainly comprise of pixel based methodologies. Surveillance is a major application of video fusio...