A Trainable Object Detection System: Car Detection in Static Images

This paper describes a general, trainable architecture for object detection that has previously been applied to face and peoplesdetection with a new application to car detection in static images. Our technique is a learning based approach that uses a set of labeled training data from which an implicit model of an object class -- here, cars -- is learned. Instead of pixel representations that may be noisy and therefore not provide a compact representation for learning, our training images are transformed from pixel space to that of Haar wavelets that respond to local, oriented, multiscale intensity differences. These feature vectors are then used to train a support vector machine classifier. The detection of cars in images is an important step in applications such as traffic monitoring, driver assistance systems, and surveillance, among others. We show several examples of car detection on out-of-sample images and show an ROC curve that highlights the performance of our system.

Support Vector Machines: Training and Applications

The Support Vector Machine (SVM) is a new and very promising classification technique developed by Vapnik and his group at AT&T Bell Labs. This new learning algorithm can be seen as an alternative training technique for Polynomial, Radial Basis Function and Multi-Layer Perceptron classifiers. An interesting property of this approach is that it is an approximate implementation of the Structural Risk Minimization (SRM) induction principle. The derivation of Support Vector Machines, its relationship with SRM, and its geometrical insight, are discussed in this paper. Training a SVM is equivalent to solve a quadratic programming problem with linear and box constraints in a number of variables equal to the number of data points. When the number of data points exceeds few thousands the problem is very challenging, because the quadratic form is completely dense, so the memory needed to store the problem grows with the square of the number of data points. Therefore, training problems arising in some real applications with large data sets are impossible to load into memory, and cannot be solved using standard non-linear constrained optimization algorithms. We present a decomposition algorithm that can be used to train SVM's over large data sets. The main idea behind the decomposition is the iterative solution of sub-problems and the evaluation of, and also establish the stopping criteria for the algorithm. We present previous approaches, as well as results and important details of our implementation of the algorithm using a second-order variant of the Reduced Gradient Method as the solver of the sub-problems. As an application of SVM's, we present preliminary results we obtained applying SVM to the problem of detecting frontal human faces in real images.

Applying the Lessons of “Lean Now” to Transform the US Aerospace Enterprise

Lean is common sense and good business sense. As organizations grow and become more successful, they begin to lose insight into the basic truths of what made them successful. Organizations have to deal with more and more issues that may not have anything to do with directly providing products or services to their customers. Lean is a holistic management approach that brings the focus of the organization back to providing value to the customer. In August 2002, Mrs. Darleen Druyun, the Principal Deputy to the Assistant Secretary of the Air Force for Acquisition and government co-chairperson of the Lean Aerospace Initiative (LAI), decided it was time for Air Force acquisitions to embrace the concepts of lean. At her request, the LAI Executive Board developed a concept and methodology to employ lean into the Air Force’s acquisition culture and processes. This was the birth of the “Lean Now” initiative. An enterprise-wide approach was used, involving Air Force System Program Offices (SPOs), aerospace industry, and several Department of Defense agencies. The aim of Lean Now was to focus on the process interfaces between these “enterprise” stakeholders to eliminate barriers that impede progress. Any best practices developed would be institutionalized throughout the Air Force and the Department of Defense (DoD). The industry members of LAI agreed to help accelerate the government-industry transformation by donating lean Subject Matter Experts (SMEs) to mentor, train, and facilitate the lean events of each enterprise. Currently, the industry SMEs and the Massachusetts Institute of Technology are working together to help the Air Force develop its own lean infrastructure of training courses and Air Force lean SMEs. The first Lean Now programs were the F/A-22, Global Hawk, and F-16. Each program focused on specific acquisition processes. The F/A-22 focused on the Test and Evaluation process; the Global Hawk focused on Evolutionary Acquisitions; and the F-16 focused on improving the Contract Closeout process. Through lean, each enterprise made many significant improvements. The F/A-22 was able to reduce its Operational Flight Plan (OFP) Preparation and Load process time of 2 to 3 months down to 7 hours. The Global Hawk developed a new production plan that increases the annual production of its Integrated Sensor Suite from 3 per year to 6 per year. The F-16 enterprise generated and is working 12 initiatives that could result in a contract closeout cycle time reduction of 3 to 7 years. Each enterprise continues to generate more lean initiatives that focus on other areas and processes within their respective enterprises.