Space Internet technology is the Internet that refers to technology which is widely used in the terrestrial network,
improved under the space communications feature, thus using the improved Internet protocols and technology, to meet
the needs of future space mission technology. In protocol engineering area, conformance testing is a very important step.
The purpose of conformance testing is to determine to what extent a single implementation of a particular standard
conforms to the individual requirements of that standard. Conformance testing can increase the confidence level of
implementation complying with the protocol specification, and enhance the probability of interoperability between
different implementation. The ISO/IEC9646 standard described by ISO is the maturest theory in protocol conformance
testing theories. Space communication network test framework is proposed, in which the test architecture is defined for
conformance testing to space communication network. In this paper, the ISO9646 standard is improved, in order to meet
the requirements of conformance testing of space communication network. The contribution of this paper is the
proposition of a complete method to the design of conformance testing against space communication network. The
simulation results have shown that the method has better test results, to meet the space communications network testing
needs. With the testing of the space communications network protocol implementation, we discovered the problems of
the protocol implementation. It has verified the theory and method.
Vehicle detection is critical to traffic surveillance and management systems. In real outdoor daylight scenes, shadows cast by moving vehicles are often detected as a part of the moving vehicles since shadows move in accordance with the movement of vehicles, which will heavily affects accuracy of vehicle detection. In this paper, an algorithm is proposed to suppress the moving cast shadow for vehicle detection based on four properties of the moving cast shadow. Simulation results indicate that the proposed algorithm can effectively suppress moving cast shadows of input image, which helps to improve accuracy of vehicle detection.
In recent years, video-based Intelligent Transportation Systems (ITS) have been of major importance for enforcing traffic management policies. Detection and tracking of moving vehicle is at the core of many applications dealing with traffic image sequences. For an accurate scene analysis in monocular image sequences, a robust segmentation of moving object from the static background is generally required. However, one of the main challenges in these applications is moving cast shadows, which often interfere with fundamental tasks such as object extraction and description. For this reason, shadow segmentation is an important step in image analysis.
We propose a real-time and effective method for detecting vehicles from a sequence of traffic images taken by a single roadside mounted camera. The proposed algorithm includes three stages: first, extract moving object region and background region from the current input image, second, by adopting the various characteristics of shadow in luminance, chrominance, and gradient density, segment moving cast shadow region which is often caused by moving vehicle and, at last, Sobel edge detector is employed to detect edge pixels of the moving cast shadow in order to suppress all shadow pixels in the detected region.
The proposed method has been tested on a number of typical monocular traffic-image sequences and the experimental results on the real-world videos show that the algorithm can effectively detect the associated moving cast shadow from the interested object.
GME (Global Motion Estimation) is an important tool widely used in computer vision, video processing, and other fields. In this paper, we propose an efficient, robust, and fast method for the estimation of global motion from compressed image sequences. With regard to global motion models, we adopt six-parameter affine model because of its reasonable tradeoff between complexity and accuracy. In order to improve accuracy and computational efficiency of global motion estimation, we present a new algorithm for segmentation between background and foreground. Then, motion vectors samples associated with background macroblocks are selected to estimate motion model parameters. Lastly, according to the statistics of estimated error, some sample pairs may be rejected as outliers to compensate further for the fact that some of the samples obtained from the P-frame motion vectors are highly erroneous and the parameters may be refined by estimating from the remaining data. The extensive experiments show that the proposed method is efficient and robust in terms of both computational complexity and accuracy.
An improved intra-prediction method for AVS-M, which is developed by AVS (Audio Video Coding Standard Working Group of China) to meet the requirements of multimedia applications in next-generation mobile communications, is proposed in this paper. Utilizing the spatial and temporal correlation of the video sequences, the proposed method only processes a portion of normative 9 intra-prediction modes to decide the best mode, therefore, speeds up intra prediction with some degradation in the coding gain. Moreover, a complexity-scalable technique is presented to control the complexity of intra prediction, by which the video coder is matched to the limited computational resource of communication devices. The proposed methods are verified. The results show that the improved mode-decision method can significantly save encoding time at expense of little coding loss and the complexity-scalable technique works robustly and accurately. In addition, the proposed method also can be applied to other standards, such as H.264.
In recent years, video-based Intelligent Transportation Systems (ITS) have been of major importance for enforcing traffic
management policies. We propose a real-time and effective method for detecting vehicles from a sequence of traffic
images taken by a single roadside mounted camera. The proposed algorithm includes three stages: first, extract moving
object region from the current input image by background subtraction method, second, eliminate moving cast shadow
which is often caused by moving vehicle and, at last, detect vehicle so that there can be a unique object associated with
The proposed method has been tested on a number of monocular traffic-image sequences and the experimental
results on the real-world videos show that the algorithm is effective and real-time. The correct rate of vehicle detection is
higher than 90 percent, independent of environmental conditions.