A Near Real-Time, Highly Scalable, Parallel and Distributed Adaptive Object Detection and Re-Training Framework Based on the Adaboost Algorithm

Object detection, such as face detection using supervised learning, often requires extensive training for the computer, which results in high execution times. If the trained system needs re-training in order to accommodate a missed detection, waiting several hours or days before the system is ready may be unacceptable in practical implementations. This dissertation presents a generalized object detection framework whereby the system can efficiently adapt to misclassified data and be re-trained within a few minutes. Our developed methodology is based on the popular AdaBoost algorithm for object detection. AdaBoost functions by iteratively selecting the best among weak classifiers, and then combining several weak classifiers in order to obtain a stronger classifier. Even though AdaBoost has proven to be very effective, its learning execution time can be high depending upon the application. For example, in face detection, learning can take several days. In our dissertation, we present two techniques that contribute to reducing to the learning execution time within the AdaBoost algorithm. Our first technique utilizes a highly parallel and distributed AdaBoost algorithm that exploits the multiple cores in a CPU via lightweight threads. In addition, our technique uses multiple machines in a web service similar to a map-reduce architecture in order to achieve a high scalability, which results in a training execution time of a few minutes rather than several days. Our second technique is a methodology to create an optimal training subset to further reduce the training execution time. We obtained this subset through a novel score-keeping of the weight distribution within the AdaBoost algorithm, and then removed the images that had a minimal effect on the overall trained classifier. Finally, we incorporated our parallel and distributed AdaBoost algorithm, along with the optimized training subset, into a generalized object detection framework that efficiently adapts and makes corrections when it encounters misclassified data. We demonstrated the usefulness of our adaptive framework by providing detailed testing on face and car detection, and explained how our framework applies to developing any other object detection task

Analyzing Taekwondo Poomsae Video Based on Background Modeling Approach

One of the most popular martial art programs in world-wide is Taekwondo. It is an official Olympic game. Over 177 countries, more than five million people world-wide practice Taekwondo as their martial art style. Specifically, the Poomsae is a series of basic movements in Taekwondo for offensive and defensive techniques. Despite the high popularity and long history of Taekwondo, there has been less effort to systemize Taekwondo Poomsae competition, which may cause judging issues and be a hurdle of its proceeding to a new game in Olympics. In this poster we will mention how to use background modeling approach in Taekwondo Poomsae videos that can help in eliminating the noises around the player which could be caused by audience. At the end, it will be very helpful in analyzing Taekwondo Poomsae captured videos

Cloud Computing: The Future of IT Industry

As a result of the research processing in the computing field, a new computing model appeared based on the development of many computing models such as parallel computing, distributed computing, and grid computing. Many normal distributed computers collaborate of achieve a function like a super computer. The computation will be assigned to this super computer rather than local computer or remote server. This is the basic concept of cloud computing. However, there is a new implementation of cloud computing was introduced based on using the internet millions of computers connected to a super cloud. Cloud computing has several advantages such as; user does not need to worry about how the cloud runs, viruses, maintenance, etc. We would expect that cloud computing is going to reshape the IT industry. In this paper we discuss cloud computing from different angles such as concept, characteristics and classifications of cloud computing

Improved Eigenface Recognition Using Hierarchal Technique

Face recognition is one of the important fields of computer vision and pattern recognition because of its applications in security and intelligence systems. In this paper we improve one of the famous algorithms used for face recognition which is Eigenface technique. The method of improvement used in this paper is the Hierarchical technique which means that input image will be applied into different levels of recognition instead of only one level as used in the regular Eigenface method. The hierarchical technique developed in this paper increases the detection rate in the case of large benchmark image database by more than 30% as compared to the original method

Highly Scalable, Parallel and Distributed AdaBoost Algorithm Using Light Weight Threads and Web Services on a Network of Multi-Core Machines

AdaBoost is an important algorithm in machine learning and is being widely used in object detection. AdaBoost works by iteratively selecting the best amongst weak classifiers, and then combines several weak classifiers to obtain a strong classifier. Even though AdaBoost has proven to be very effective, its learning execution time can be quite large depending upon the application e.g., in face detection, the learning time can be several days. Due to its increasing use in computer vision applications, the learning time needs to be drastically reduced so that an adaptive near real time object detection system can be incorporated. In this paper, we develop a hybrid parallel and distributed AdaBoost algorithm that exploits the multiple cores in a CPU via light weight threads, and also uses multiple machines via a web service software architecture to achieve high scalability. We present a novel hierarchical web services based distributed architecture and achieve nearly linear speedup up to the number of processors available to us. In comparison with the previously published work, which used a single level master-slave parallel and distributed implementation [1] and only achieved a speedup of 2.66 on four nodes, we achieve a speedup of 95.1 on 31 workstations each having a quad-core processor, resulting in a learning time of only 4.8 seconds per feature

Survey on Robust Object Detection

This poster presents a well known object detection technique, which has been used for long time. Viola-Jones approach, is a real time object detection algorithm, which is mostly used for face detection. In Viola-Jones algorithm there are three main contributions: First, inventing a new technique for features computation, which is called Integral Image. Second, using Adaboost learning algorithm to build complex classifier from simple ones. Third, design complex classifier from a strong classifiers, produced from the learning algorithms, by using cascade structure in order. The main goal of our work, is to implement Viola-Jones approach using Matlab and C# programming language, then enhance it by using parallel programming techniques which are supported by .NET framework under this name: Task Parallel Library (TPL)

Parallel and Distributed Object Recognition

This poster presents a parallel implementation of an object detection algorithm, as well as an improved pruning technique, which is an important part in an object detection implementation. We focus on face detection, even though the techniques developed are applicable to object detection in general. We implement the well known face detection algorithm of Viola-Jones and parallelize the important steps, and then come up with a better pruning algorithm when many nearby windows indicate a detection. We also consider the effect of multiple scales and present a pruning algorithm that minimizes the detected windows such that a single window indicating the presence of a detected face can be concluded. Our pruning algorithm maximizes the face detection such that a few false positives may be detected, but all faces present are correctly identified