An Efficient and Cost Effective FPGA Based Implementation of the Viola-Jones Face Detection Algorithm

We present an field programmable gate arrays (FPGA) based implementation of the popular Viola-Jones face detection algorithm, which is an essential building block in many applications such as video surveillance and tracking. Our implementation is a complete system level hardware design described in a hardware description language and validated on the affordable DE2-115 evaluation board. Our primary objective is to study the achievable performance with a low-end FPGA chip based implementation. In addition, we release to the public domain the entire project. We hope that this will enable other researchers to easily replicate and compare their results to ours and that it will encourage and facilitate further research and educational ideas in the areas of image processing, computer vision, and advanced digital design and FPGA prototyping

Speeding up Adaboost object detection with motion segmentation and Haar feature acceleration

A key challenge in a surveillance system is the object detection task. Object detection in general is a non-trivial problem. A sub-problem within the broader context of object detection which many researchers focus on is face detection. Numerous techniques have been proposed for face detection. One of the better performing algorithms is proposed by Viola et. al. This algorithm is based on Adaboost and uses Haar features to detect objects. The main reason for its popularity is very low false positive rates and the fact that the classifier network can be trained for any detection task. The use of Haar basis functions to represent key object features is the key to its success. The basis functions are organized as a network to form a strong classifier. To detect objects, this technique divides each input image into non-overlapping sub-windows and the strong classifier is applied to each sub-window to detect the presence of an object. The process is repeated at multiple scales of the input image to detect objects of various sizes. In this thesis we propose an object detection system that uses object segmentation as a preprocessing step. We use Mixture of Gaussians (MoG) proposed by Staffer et. al. for object segmentation. One key advantage with using segmentation to extract image regions of interest is that it reduces the number of search windows sent to detection task, thereby reducing the computational complexity and the execution time. Moreover, owing to the computational complexity of both the segmentation and detection algorithms we used in the system, we propose hardware architectures for accelerating key computationally intensive blocks. In this thesis we propose hardware architecture for MoG and also for a key compute intensive block within the adaboost algorithm corresponding to the Haar feature computation

Drowziness Detection System using Image Processing

There have been a lot of approaches in the detection of drowsiness. The parameters taken into consideration were the eye opening window, the number of blinks during a time period, no. of yawns etc. there are about 12 facial features that can be determined by the camera mounted on the circuit board. The parameter considered here is only the window of eye opening. In addition to the 12 parameters, the head motion was also taken into consideration which, in turn, contributed to the improvement of the accuracy of the measurement. Driver Drowsiness is one of the real reasons for mishaps on the planet. In this undertaking I plan to build up a model of drowsiness recognition framework. This framework meets expectations by observing the eyes of the driver and sounding a caution when he/she is tired. The framework so outlined is a non-nosy continuous checking framework. The need is on enhancing the security of the driver without being prominent. In this venture the eye flicker of the driver is recognized. In the event that the drivers’ eyes stay shut for more than a certain duration of time, the driver is said to be languid and an alert is sounded. The programming for this is done in matlab using image acquisition tool

Optimization of common computer vision algorithms : beating OpenCV face detector

In this Master Thesis one of the most common problems related to face detection is presented: fast and accurate unconstrained face detection. To deal with this problem a new general learning method is presented. The proposed method introduces a set of upgrades and modifications on key concepts and ideas of Decision Trees, AdaBoost and Soft Cascade learning techniques. Firstly, a new variation of Decision Trees with quadratic thresholds able to maximize the margin distance between classes is introduced. Considering a training set independent of face orientation and viewpoints information, the proposed algorithm is able to learn a combination of features to cluster faces under unconstrained face position and orientation. Next, a new definition of the Soft Cascade thresholds training principles is provided. Hence, this modification leads to a better formulation of the loss function associated to the AdaBoost algorithm. The trained face detector has been tested over the Face Detection Data Set and Benchmark (FDDB) and compared against the current state of the art classifiers. The obtained results show that the proposed face detector (i) is able to detect faces with unconstrained position, and (ii) it works faster than the current state of the art method