An immune optimization algorithm is proposed in this paper based on the immune negative selection. The algorithm NSIOA is motivatedby the negative selection mechanism in biological immune recognition. Different from the existing immune optimization methods,NSIOA constantly removes the worst solutions to get the optimal solution. Considering that removal of poor members of a populationmight lead to the loss of design information that may actually help identify better solutions in the search space, the proposed NSIOA isdesigned to keep the diversity of antibodies while removing poor members, therefore the algorithm will converge to global optimalsolution with high probability. The convergence property and the complexity of the algorithm have also been analyzed. To illustratethe efficiency of the algorithm is used in solving the travel salesman problem. The theoretical analysis and experimental results show thatthe algorithm is of a strong potential in solving practical problems.

The ultimate purpose of a pedestrian-detectionsystem (PDS) is to reduce pedestrian-vehicle-related injury. Mostsuch systems tend to adopt expensive sensors, such as infrareddevices, in expectation of better performance. In comparison, alow-cost optical-camera-based system has much potential practicalvalue, including a greater detection range, and can easily betrained to detect other objects. However, such low-cost systems aredifficult to design (e.g., little original information can be collected,and the scene is very complex). To address these problems, aneffective and reliable classifier is needed. The classifier should havea proper structure, its features need to be well selected, and a largenumber of high-quality samples are necessary for training. In thispaper, we present a low-cost PDS which only uses a single opticalcamera. We design a cascade classifier to achieve an effective andreliable detection. First, our system scans two sequential framesat each zoom scale with a sliding window. Second, with eachwindow, both appearance and motion features are extracted. Awell-trained cascade classifier, combining statistical learning witha decomposed support-vector-machine classifier, then determineswhether the window contains a human body. At the same time,to provide as much information as possible about the pedestrian,a small-scale weighted template tree trained by a coevolutionaryalgorithm is adopted to identify each pedestrian’s direction, andthe distance of each from the vehicle is also provided using anestimation algorithm. During the training procedure, we select keyfeatures by using the AdaBoost algorithm and a large numberof high-quality samples. Experimental results demonstrate thatthe system is suitable for pedestrian detection in city traffic: Thedetection speed is more than 10 ft/s, the detection rate reaches80%, and the false positive rate is no more than 0.3‰.

In a pedestrian detection system, a classifier isusually designed to recognize whether a candidate is apedestrian. Support vector machines (SVM) have become aprimary technique to train a classifier for pedestrian detection.However, it is hard to give the best training model which has atremendous effect to the performance of a SVM classifier. In thispaper, we design special code/decode scheme and evaluationfunction for a training model firstly; and then use geneticalgorithm to optimize key parameters which represent the SVMtraining model. Therefore a most suitable SVM classifier can beobtained for pedestrian detection. Experiments have been carriedout in a single camera based pedestrian detection system. Theresults show that the evolutionary SVM classifier has a betterdetection rate; moreover, RBF kernel is more suitable thanpolynomial kernel when chosen in an evolutionary SVMclassifier for pedestrian detection.

This paper proposes a coevolutionary optimization algorithm called DCOA.DCOA mainly focuses on how to adjust sub-population size self-adaptively so as to improvethe optimizing performance. To achieve this, a strategy is introduced which consistsof three rules: internal competition, external competition and spontaneous growthrules. These rules can control individual reproduction and elimination speed in each subpopulation.Furthermore, the adjustment can be proven globally asymptotically stable. Inthe experiments, we compare the performances of DCOA, macroevolutionary algorithm(MA) [13] and simple genetic algorithm (SGA) with typical test functions. The resultsshow that DCOA is able to find the global optimum on most difficult functions, nothingless than MA which uses simulated annealing technique. At the same time, DCOA convergesquickly, similar to SGA and faster than MA.

Until now, existing pedestrian detection systemsusually use global features (e.g. appearance or motion) of humanbody to detect pedestrian; however, the detection rate needs to beimproved in many situations since sometimes the global featurescan not be obtained. For example, a pedestrian may be partlycovered by a car or his/her part may hide into the background.Therefore it is essential to adopt some local features of key partsof human body to assist pedestrian detection.In this paper, we propose a method using some key localfeatures of human body to help pedestrian detection. Since theintroduction of additional features will cost the system more time,in order to ensure the detection speed, we firstly use bothappearance and motion global features of human body to selectcandidates, and then use local features of head and leg to dofurther confirmation. In the confirmation stage, we use threekinds of local features (head appearance, face color and haircolor) to detect the head of each candidate; at the same time, wealso choose some particular local appearance features to detectthe leg. The experimental results indicate that this method canimprove detection rate with almost the same detection speed;additionally, it can reduce false alarm sometimes.