There has been a growth in demand for advancing algorithms in surveillance applications concerning moving vehicles where analysis of traffic has a potential application to security, traffic management (congestion and accident detection), speed measurement, car counting and statistics, as well as turning movement at intersections. This research focuses on multiple-vehicle detection, recognition,...

Automatic license/number plate recognition is a specific application of optical character recognition. Typically employed by law enforcement agencies, the uses for automatic license plate recognition have grown tremendously since its inception. Automatic license plate recognition may be used to cite individuals who violate traffic signals or drive in excess of the speed limit, as a method of el...

We propose a novel model-based approach to activity recognition using high-level primitives that are derived from a human body model estimated from sensor data. Using short but fixed positions of the hands and turning points of hand movements, a continuous data stream is segmented in short segments of interest. Within these segments, joint boosting enables the automatic discovery of important a...

In this proposed embedded car security system, FDS (Face Detection System) is used to detect the face of the driver and compare it with the predefined face. For example, in the night when the car’s owner is sleeping and someone theft the car then FDS obtains images by one tiny web camera which can be hidden easily in somewhere in the car. FDS compares the obtained image with the predefined imag...

In this paper, we propose a named-entity recognition (NER) system that addresses two major limitations frequently discussed in the field. First, the system requires no human intervention such as manually labeling training data or creating gazetteers. Second, the system can handle more than the three classical named-entity types (person, location, and organization). We describe the system’s arch...

Object class recognition in a given image is a difficult problem. To classify a test image as hit or match for a particular object class requires the abstraction of an object, developed using real world images. This is where the difficulty lies since the images of an object class can have large-scale variations in terms of illumination, angle of view, scale, rotation, color, location, type etc ...

This paper proposes to go beyond the state-of-the-art deep convolutional neural network (CNN) by incorporating the information from object detection, focusing on dealing with fine-grained image classification. Unfortunately, CNN suffers from over-fiting when it is trained on existing finegrained image classification benchmarks, which typically only consist of less than a few tens of thousands t...