Research On Detection And Recognition Of Traffic Objects In City Environments

Driving in city enviroments, the unmanned intelligent vechile needs to detect and recognize the objects in the image data, such as road, front vehicle, brake light and turn light of front vehicle, traffic light and so on. On the one hand, because of space limit, the unmanned intelligent vehicle’s computing combability is limited; on the other hand, owing to the development of camera device, image data is bigger than past, and with the increasingly complex, image processing algotithm needs more computing resource. It is hard to decide between them. But, the best way to resolve this problem is to construct the hybrid heterogeneous parallel computing model to utilize the computing resource sufficiently. All in all, in this paper, some works have been done as below.1. Due to the threshold of segmentation is hard to get, a number of classes selection algorithm in multi-class maximum variance menthod is designed to get multi-thresholds for segmentation. Binarization image including road region selection algorithm and on the basis of quadratic polynomial piecewise function fitness method are designed to estimate the edge of road. According to analysis of shape of the shadow underneath vehicles, some constraint conditions are designed for detecting the positions of front vehicles in image. Experiment results shows that these methods’ availability.2. By the mirror feature of real lamps, pairing real lamps detection algorithm is designed, and the shadow of front vehicles are cooperated with it for locating the vehicles. According to the relative relation among positions of regions, a multi-objects Kalman filter tracking algorithm is designed for increasing the detection rate of pairing rear lamps. In order to get the deceleration message of front vehicle, two algoritm are combined to detect and recognize. One is the high location brake light detection algorithm; the other is brightness state of brake light recognition algorithm according to the distribution of the color when the lamp is lit. When the left or right turn lamps are flashing, them send different messages to the rear drivers, respectively. If the pairing rear lamps detection is invalid, an algorithm named turn lamps left-right diviced method is designed. To recognize if the turn lamps being in the process of "extinguished"-"lit"-"extinguished" states conversion, a turn lamp flashing state accumulated recognition algorithm is designed. Experiments results show that these algorithms are effective.3. According to the light source of traffic light which has the feature of "bright", and back board of it which has the feature of "dark", two algorithms are designed to abstract the regions which are matched each others for locating the traffic lights. The one is "bright" regions abstraction algorithm based on Top-hat operator, and the other is "dark" regions abstraction algorithm based on multi-thresholds segmentation.To recognize if the regions are circular light source of circle traffic light, circurity method and improved Hough circle detection are designed. The standard arrow shape area projected functions are established. Then, the sample of region is projected and normalized. The variance between the reality value and estimated value is the key criterion to recognize if the regions are arrow light source. Experiments results show that these algorithms are effective.4. A hybrid heterogeneous on-board parallel computing model is introduced. The topological structure of on-board networked computers is constructed, and the task distribution and Job scheduling algorithm on the basis of on-line dynamic analysis is designed. The GPU+CPU load balancing optimization strategy is presented to reduce the data exchange. The whole detection and recognition system is divided by tasks for stratification management, and every tasks is parallel designed at algorithm level. The processes above accomplish hybrid heterogeneous parallel design at both coarse-grained and fine-grained.