Development of an Automatic Tracking Camera System Integrating Image Processing and Machine Learning

This paper describes the development of a robust object tracking system that combines detection methods based on image processing and machine learning for automatic construction machine tracking cameras at unmanned construction sites. In recent years, unmanned construction technology has been developed to prevent secondary disasters from harming workers in hazardous areas. There are surveillance cameras on disaster sites that monitor the environment and movements of construction machines. By watching footage from the surveillance cameras, machine operators can control the construction machines from a safe remote site. However, to control surveillance cameras to follow the target machines, camera operators are also required to work next to machine operators. To improve efficiency, an automatic tracking camera system for construction machines is required. We propose a robust and scalable object tracking system and robust object detection algorithm, and present an accurate and robust tracking system for construction machines by integrating these two methods. Our proposed image-processing algorithm is able to continue tracking for a longer period than previous methods, and the proposed object detection method using machine learning detects machines robustly by focusing on their component parts of the target objects. Evaluations in real-world field scenarios demonstrate that our methods are more accurate and robust than existing off-the-shelf object tracking algorithms while maintaining practical real-time processing performance.

Multi-horizon Simultaneous Tracking Based on Dynamic Seismic Waveform Matching

Summary Due to the rapid increase in the amount of seismic volumes, the traditional seismic interpretation mode based on manual structure interpretation and single-horizon automatic tracking has encountered many challenges. The seismic interpretation of large or super-large 3-D seismic surveys is facing serious accuracy and efficiency bottlenecks. Aiming to the goal of improving the accuracy and efficiency of seismic interpretation, we propose a dynamic seismic waveform matching technology based on the sparse dynamic time warping algorithm under the guidance of the relative geological time volume theory, and realize multi-horizon simultaneous tracking based on the technology. Has been verified by a model and a real seismic volume, it can realize simultaneous horizon automatic tracking, full spatial tracking and high-density tracking, and can significantly improve the accuracy and efficiency of structure interpretation.

Automatic Tracking Method of Basketball Flight Trajectory Based on Data Fusion and Sparse Representation Model

The appearance model of flying basketball obtained by the traditional basketball flight trajectory tracking method is not accurate, which leads the anti-interference performance of trajectory tracking not ideal. Based on data fusion and sparse representation model, a new automatic trajectory tracking method is proposed. Firstly, the relevant technologies of basketball flight trajectory automatic tracking are studied and summarized, and then the method is studied. The specific implementation steps of this method are as follows: the features of flying basketball images were extracted by the target feature extraction algorithm, and the appearance model of flying basketball was built based on sparse representation. Data fusion technology and particle filter algorithm are combined to realize automatic tracking of basketball flight path. Through three axial basketball trajectories of automatic tracking test and noise test and verify the design method under the 3D world coordinate system to achieve the X, Y, and Z axis up more accurate tracking, at the same time, after applying measurement signal to noise, automatic trajectory tracking results affected by some, but still managed to realize the trajectory tracking.

Caenorhabditis elegans Multi-Tracker Based on a Modified Skeleton Algorithm

Automatic tracking of Caenorhabditis elegans (C. egans) in standard Petri dishes is challenging due to high-resolution image requirements when fully monitoring a Petri dish, but mainly due to potential losses of individual worm identity caused by aggregation of worms, overlaps and body contact. To date, trackers only automate tests for individual worm behaviors, canceling data when body contact occurs. However, essays automating contact behaviors still require solutions to this problem. In this work, we propose a solution to this difficulty using computer vision techniques. On the one hand, a skeletonization method is applied to extract skeletons in overlap and contact situations. On the other hand, new optimization methods are proposed to solve the identity problem during these situations. Experiments were performed with 70 tracks and 3779 poses (skeletons) of C. elegans. Several cost functions with different criteria have been evaluated, and the best results gave an accuracy of 99.42% in overlapping with other worms and noise on the plate using the modified skeleton algorithm and 98.73% precision using the classical skeleton algorithm.

Study on Automatic Tracking Method of Marking Points in Sports Image Sequence

: The effect of teaching and training in physical sports is improved by the sport demonstration system. The two-dimensional sport demonstration system is widely applied for the training of practical athletic. In the accurate motion positioning, a certain visual deficiency exists, and the two-dimensional sport demonstration system is analyzed by the kinematic. Methodology: Aiming at the problems in the real-time tracking of fast moving targets in sports images, an automatic tracking method of sports images, based on the registration of landmark points of the passive optical motion capture system, is proposed. First, build a human model and divide the human model into several limb segments. Then, find a corresponding relationship between the first frame of motion data and template data to complete the first frame of motion data registration; based on the smallest non-rigid deformation and point, set matching error to find the corresponding relationship between the current frame of motion data and the previous frame of registered motion data. Finally, through the mark points, follow up to complete the registration process of the marker. Results: Experiments show that the average processing accuracy of this algorithm can reach over 85%, and the processing time of a single frame of motion data is t<1/60s, which can meet real-time requirements. Conclusion: The multi-point set least squares matching algorithm is used to correct the registered landmark data rigidly. No manual intervention is required for the entire mark registration process.