Target recognition for coastal surveillance based on radar images and generalised Bayesian inference

For coastal surveillance, this study proposes a novel approach to identify moving vessels from radar images with the use of a generalised Bayesian inference technique, namely the evidential reasoning (ER) rule. First of all, the likelihood information about radar blips is obtained in terms of the velocity, direction, and shape attributes of the verified samples. Then, it is transformed to be multiple pieces of evidence, which are formulated as generalised belief distributions representing the probabilistic relationships between the blip's states of authenticity and the values of its attributes. Subsequently, the ER rule is used to combine these pieces of evidence, taking into account their corresponding reliabilities and weights. Furthermore, based on different objectives and verified samples, weight coefficients can be trained with a non-linear optimisation model. Finally, two field tests of identifying moving vessels from radar images have been conducted to validate the effectiveness and flexibility of the proposed approach

Segmentasi dan pengesanan objek bergerak dalam keadaan cuaca berjerebu dan berkabus

Segmentation and detection of moving object are very important in navigation applications to improve visibility of computer vision technology. The challenges to these issues are how these two issues address hazy and foggy weather. This situation affects technology and specifically the video data used to detect moving objects. This problem occurs due to the light that is scattered because of the fog and haze pixels which prevent light from penetrating resulting in over segmentation. Various methods have been used to improve accuracy and sensitivity in over segmentation but further enhancement is needed to improve the performance in the detection of moving objects. In this research, a new method is proposed to overcome over segmentation which is a combination between Gaussian Mixture Model and other filters based on their own specialities. The combined filters comprised Median Filter and Average Filter for over segmentation, Morphology Filter and Gaussian Filter to rebuild structure element of pixel object, and combination of Blob Analysis, Bounding Box and Kalman Filter to reduce False Positive detection. The combination of these filters is known as Object of Interest Movement (OIM). Qualitative and quantitative methods were used to make comparison with previous methods. Data comprised sources of haze recordings obtained from YouTube and open dataset from Karlsure. Comparative analysis of pictures and calculations of detection of objects were done. Result showed that the combined filters is capable of improving accuracy and sensitivity of the segmentation and detection which were 72.24% for foggy videos, and 76.73% in hazy weather. Based on the findings, the OIM method has proven its capability to improve the accuracy of segmentation and detection object without the need for enhancement to contrast an image

Adaptive background subtraction technique with unique feature representation for vehicle counting

Vehicle detection is the first step towards a successful traffic monitoring system. Although there were many studies for vehicle detection, only a few methods dealt with a complex situation especially in traffic jams. In addition, evaluation under different weather conditions (rainy, foggy and snowy) is so important for some countries but unfortunately it is rarely performed. Presently, vehicle detection is mainly performed using background subtraction method, yet it still faces many challenges. In this thesis, an adaptive background model based on the approximate median filter (AMF) is developed. To demonstrate its potential, the proposed method is further combined with two proposed feature representation techniques to be employed in either global or local vehicle detection strategy. In the global approach, an adaptive triangle-based threshold method is applied following the proposed adaptive background method. As a consequence, a better segmented foreground can be differentiated from the background regardless of the different weather conditions (i.e., rain, fog and snowfall). Comparisons with the adaptive local threshold (ALT) and the three frame differencing methods show that the proposed method achieves the average recall value of 85.94% and the average precision value of 79.53% with a negligible processing time difference. In the local approach, some predefined regions, instead of the whole image, will be used for the background subtraction operation. Subsequently, two feature representations, i.e. normalized object-area occupancy and normalized edge pixels are computed and formed into a feature vector, which is then fed into the k-means clustering technique. As illustrated in the results, the proposed method has shown an increment of at least 10% better in terms of the precision and 4.5% in terms of F1 score when compared to the existing methods. Once again, even with this significant improvement, the proposed method does not incur noticeable difference in the processing time. In conducting the experiments, different standard datasets have been used to show the performance of the proposed approach. In summary, the proposed method has shown better performances compared to three frame differencing and adaptive local threshold methods

Street Viewer: An Autonomous Vision Based Traffic Tracking System

The development of intelligent transportation systems requires the availability of both accurate traffic information in real time and a cost-effective solution. In this paper, we describe Street Viewer, a system capable of analyzing the traffic behavior in different scenarios from images taken with an off-the-shelf optical camera. Street Viewer operates in real time on embedded hardware architectures with limited computational resources. The system features a pipelined architecture that, on one side, allows one to exploit multi-threading intensively and, on the other side, allows one to improve the overall accuracy and robustness of the system, since each layer is aimed at refining for the following layers the information it receives as input. Another relevant feature of our approach is that it is self-adaptive. During an initial setup, the application runs in learning mode to build a model of the flow patterns in the observed area. Once the model is stable, the system switches to the on-line mode where the flow model is used to count vehicles traveling on each lane and to produce a traffic information summary. If changes in the flow model are detected, the system switches back autonomously to the learning mode. The accuracy and the robustness of the system are analyzed in the paper through experimental results obtained on several different scenarios and running the system for long periods of time