Single Shot MultiBox Detector (SSD) is one of the fastest algorithms in the current object detection field, which uses fully convolutional neural network to detect all scaled objects in an image. Deconvolutional Single Shot Detector (DSSD) is an approach which introduces more context information by adding the deconvolution module to SSD. And the mean Average Precision (mAP) of DSSD on PASCAL VOC2007 is improved from SSD’s 77.5% to 78.6%. Although DSSD obtains higher mAP than SSD by 1.1%, the frames per second (FPS) decreases from 46 to 11.8. In this paper, we propose a single stage end-to-end image detection model called ESSD to overcome this dilemma. Our solution to this problem is to cleverly extend better context information for the shallow layers of the best single stage (e.g. SSD) detectors. Experimental results show that our model can reach 79.4% mAP, which is higher than DSSD and SSD by 0.8 and 1.9 points respectively. For 300×300 input, our testing speed is 25 FPS in single Nvidia Titan X GPU which is more than the original execution speed of DSSD.
Fully convolutional networks (FCNs) have shown outstanding performance in image semantic segmentation, which is the key work in license plate detection (LPD). An FCN architecture for LPD is presented. First, a multiscale hierarchical network structure is used to combine multiscale and multilevel features produced by FCN. Then, an enhanced loss structure that contains three loss layers is defined to emphasize the license plates in images. Finally, the FCN generates prediction maps that directly show the location of license plates. Experiments show that our approach is more accurate than many state-of-the-art methods.
A vehicle detection and tracking system is one of the indispensable methods to reduce the occurrence of traffic accidents. The nearest vehicle is the most likely to cause harm to us. So, this paper will do more research on about the nearest vehicle in the region of interest (ROI). For this system, high accuracy, real-time and intelligence are the basic requirement. In this paper, we set up a system that combines the advanced KCF tracking algorithm with the HaarAdaBoost detection algorithm. The KCF algorithm reduces computation time and increase the speed through the cyclic shift and diagonalization. This algorithm satisfies the real-time requirement. At the same time, Haar features also have the same advantage of simple operation and high speed for detection. The combination of this two algorithm contribute to an obvious improvement of the system running rate comparing with previous works. The detection result of the HaarAdaBoost classifier provides the initial value for the KCF algorithm. This fact optimizes KCF algorithm flaws that manual car marking in the initial phase, which is more scientific and more intelligent. Haar detection and KCF tracking with Histogram of Oriented Gradient (HOG) ensures the accuracy of the system. We evaluate the performance of framework on dataset that were self-collected. The experimental results demonstrate that the proposed method is robust and real-time. The algorithm can effectively adapt to illumination variation, even in the night it can meet the detection and tracking requirements, which is an improvement compared with the previous work.
The performance and robustness of fatigue detection largely decrease if the driver with glasses. To address this issue, this paper proposes a practical driver fatigue detection method based on face alignment at 3000 FPS algorithm. Firstly, the eye regions of the driver are localized by exploiting 6 landmarks surrounding each eye. Secondly, the HOG features of the extracted eye regions are calculated and put into SVM classifier to recognize the eye state. Finally, the value of PERCLOS is calculated to determine whether the driver is drowsy or not. An alarm will be generated if the eye is closed for a specified period of time. The accuracy and real-time on testing videos with different drivers demonstrate that the proposed algorithm is robust and obtain better accuracy for driver fatigue detection compared with some previous method.
Segmentation of moving objects from video sequences is the fundamental step in intelligent surveillance applications. Numerous methods have been proposed to obtain object segmentation. In this paper, we present an effective approach based on the mixture of Gaussians. The approach makes use of a feedback strategy with multiple levels: the pixel level, the region level, and the frame level. Pixel-level feedback helps to provide each pixel with an adaptive learning rate. The maintenance strategy of the background model is adjusted by region-level feedback based on tracking. Frame-level feedback is used to detect the global change in scenes. These different levels of feedback strategies ensure our approach’s effectiveness and robustness. This is demonstrated through experimental results on the Change Detection 2014 benchmark dataset.
Hand tracking is becoming more and more popular in the field of human-computer interaction (HCI). A lot of studies in this area have made good progress. However, robust hand tracking is still difficult in long-term. On-line learning technology has great potential in terms of tracking for its strong adaptive learning ability. To address the problem we combined an on-line learning technology called on-line boosting with an off-line trained detector to track the hand. The contributions of this paper are: 1) we propose a learning method with an off-line model to solve the drift of on-line learning; 2) we build a framework for hand tracking based on the learning method. The experiments show that compared with other three methods, the proposed tracker is more robust in the strain case.
A panorama parking assistant system (PPAS) for the automotive aftermarket together with a practical improved particle swarm optimization method (IPSO) are proposed in this paper. In the PPAS system, four fisheye cameras are installed in the vehicle with different views, and four channels of video frames captured by the cameras are processed as a 360-deg top-view image around the vehicle. Besides the embedded design of PPAS, the key problem for image distortion correction and mosaicking is the efficiency of parameter optimization in the process of camera calibration. In order to address this problem, an IPSO method is proposed. Compared with other parameter optimization methods, the proposed method allows a certain range of dynamic change for the intrinsic and extrinsic parameters, and can exploit only one reference image to complete all of the optimization; therefore, the efficiency of the whole camera calibration is increased. The PPAS is commercially available, and the IPSO method is a highly practical way to increase the efficiency of the installation and the calibration of PPAS in automobile 4S shops.
Automotive Active Safety(AAS) is the main branch of intelligence automobile study and pedestrian detection is the key
problem of AAS, because it is related with the casualties of most vehicle accidents. For on-board pedestrian detection
algorithms, the main problem is to balance efficiency and accuracy to make the on-board system available in real scenes,
so an on-board pedestrian detection and warning system with the algorithm considered the features of side pedestrian is
The system includes two modules, pedestrian detecting and warning module. Haar feature and a cascade of stage
classifiers trained by Adaboost are first applied, and then HOG feature and SVM classifier are used to refine false
positives. To make these time-consuming algorithms available in real-time use, a divide-window method together with
operator context scanning(OCS) method are applied to increase efficiency. To merge the velocity information of the
automotive, the distance of the detected pedestrian is also obtained, so the system could judge if there is a potential
danger for the pedestrian in the front. With a new dataset captured in urban environment with side pedestrians on zebra,
the embedded system and its algorithm perform an on-board available result on side pedestrian detection.