Abstract: The present disclosure provides a target object tracking method. In this disclosure, the point cloud data sequence in the target scene is collected by lidars and the first frame of cloud point data is extracted; the bounding box of the target object in the first frame of cloud point data is determined using the preset three-dimensional target tracking algorithm. All the target point clouds in the bounding box are extracted to generate the first template point cloud. The template expansion processing is performed using the preset ATG algorithm to obtain the second template point cloud. The point cloud features in the search area of other frames of point cloud data are extracted, and are compared with the second template point cloud and the reference template point cloud to obtain the comparison result. Based on the comparison result, the tracking result of the target object is generated and displayed.

Abstract: A data sparse projection method, includes: randomly initializing a high-dimensional sparse two-dimensional matrix (S1); fixing the high-dimensional sparse two-dimensional matrix, and calculating an optimal output variable by using the high-dimensional sparse two-dimensional matrix (S2); fixing the optimal output variable, and calculating an optimal high-dimensional sparse two-dimensional matrix by using the optimal output variable (S3); and cyclically fixing the high-dimensional sparse two-dimensional matrix and the output variable until the optimal output variable is no longer increased when the high-dimensional sparse two-dimensional matrix is fixed (S4).

Abstract: A data sparse projection method, includes: randomly initializing a high-dimensional sparse two-dimensional matrix (S1); fixing the high-dimensional sparse two-dimensional matrix, and calculating an optimal output variable by using the high-dimensional sparse two-dimensional matrix (S2); fixing the optimal output variable, and calculating an optimal high-dimensional sparse two-dimensional matrix by using the optimal output variable (S3); and cyclically fixing the high-dimensional sparse two-dimensional matrix and the output variable until the optimal output variable is no longer increased when the high-dimensional sparse two-dimensional matrix is fixed (S4).

Abstract: The present invention comprises methods and systems of a network of sensor devices to monitor environmental conditions. Each sensor device is capable of acquiring environmental data and transmitting the data to a central controller of a networking system by wireless communication. By processing the environmental data obtained from the geographically deployed sensor devices, the central controller is capable of detecting a trend of the hazardous condition. The central controller generates early warning signals based on the hazardous levels of the physical or environmental conditions, as well as the trend of such conditions. When receiving a high level of hazardous conditions from one of the networked sensor devices, the central controller can compare the results with neighboring sensor devices to determine whether the signal received is due to a hazard leakage or a sensor device malfunction, so to reduce false alarms and provide feedbacks to communication devices networked in the system.