To solve the problem of strong nonlinear and motion model switching of maneuvering target tracking system in clutter environment, a novel maneuvering multi-target tracking algorithm based on multiple model particle fi...To solve the problem of strong nonlinear and motion model switching of maneuvering target tracking system in clutter environment, a novel maneuvering multi-target tracking algorithm based on multiple model particle filter is presented in this paper. The algorithm realizes dynamic combination of multiple model particle filter and joint probabilistic data association algorithm. The rapid expan- sion of computational complexity, caused by the simple combination of the interacting multiple model algorithm and particle filter is solved by introducing model information into the sampling process of particle state, and the effective validation and utilization of echo is accomplished by the joint proba- bilistic data association algorithm. The concrete steps of the algorithm are given, and the theory analysis and simulation results show the validity of the method.展开更多
If a somewhat fast moving object exists in a complicated tracking environment, snake's nodes may fall into the inaccurate local minima. We propose a mean shift snake algorithm to solve this problem. However, if th...If a somewhat fast moving object exists in a complicated tracking environment, snake's nodes may fall into the inaccurate local minima. We propose a mean shift snake algorithm to solve this problem. However, if the object goes beyond the limits of mean shift snake module operation in suc- cessive sequences, mean shift snake's nodes may also fall into the local minima in their moving to the new object position. This paper presents a motion compensation strategy by using particle filter; therefore a new Particle Filter Mean Shift Snake (PFMSS) algorithm is proposed which combines particle filter with mean shift snake to fulfill the estimation of the fast moving object contour. Firstly, the fast moving object is tracked by particle filter to create a coarse position which is used to initialize the mean shift algorithm. Secondly, the whole relevant motion information is used to compensate the snake's node positions. Finally, snake algorithm is used to extract the exact object contour and the useful information of the object is fed back. Some real world sequences are tested and the results show that the novel tracking method have a good performance with high accuracy in solving the fast moving problems in cluttered background.展开更多
To cope with the problem of tracking a human head in a complicated scene,we propose a method that adopts human skin color and hair color integrated with a kind of particle filter named condensation algorithm.Firstly,a...To cope with the problem of tracking a human head in a complicated scene,we propose a method that adopts human skin color and hair color integrated with a kind of particle filter named condensation algorithm.Firstly,a novel method is presented to set up human head color model using skin color and hair color separately based on region growing.Compared with traditional human face model,this method is more precise and works well when human turns around and the face disappears in the image.Then a novel method is presented to use color model in condensation algorithm more effectively.In this method,a combination of edge detection result,color segmentation result and color edge detection result in an Omega window is used to measure the scale and position of human head in condensation.Experiments show that this approach can track human head in complicated scene even when human turns around or the distance of tracking a human head changes quickly.展开更多
At present, the ballistic Target tracking has a higher demand in convergence rate and tracking precision of filter algorithm. In the paper, a filter algorithm was improved based on particle filter. The algorithm was c...At present, the ballistic Target tracking has a higher demand in convergence rate and tracking precision of filter algorithm. In the paper, a filter algorithm was improved based on particle filter. The algorithm was carried out from the aspects such as particle degradation and particle diversity lack. A novel ballistic coefficient parameter model was built, and was expanded to the state vector for filtering. Finally, the improved algorithm was simulated by MATLAB software. The simulation results show that the algorithm can obtain better convergence speed and tracking precision.展开更多
An adaptive object tracking algorithm based on particle filtering and a modified Gradient Vector Flow (GVF) Snake is proposed for tracking moving and deforming objects. The original contours of objects are obtained by...An adaptive object tracking algorithm based on particle filtering and a modified Gradient Vector Flow (GVF) Snake is proposed for tracking moving and deforming objects. The original contours of objects are obtained by using the background differencing method,and the true contours of objects can be converged by means of the powerful searching ability of a modified GVF-Snake. Finally,an Energetic Particle Filtering (EPF) algorithm is obtained by combining particle filtering and a modified GVF-Snake,and by using K-means and the EPF algorithm,multiple objects can be tracked. The proposed tracking tactic for partially occluded objects can effectively improve its anti-occlusion ability. Experiments show that this algorithm can obtain better tracking effect even though the tracked object is occluded.展开更多
We propose an algorithm of embedding ensemble tracking in a stochastic framework to achieve robust tracking performance under partial occlusion,illumination changes,and abrupt motion.It operates on likelihood images g...We propose an algorithm of embedding ensemble tracking in a stochastic framework to achieve robust tracking performance under partial occlusion,illumination changes,and abrupt motion.It operates on likelihood images generated by the ensemble method,and combines mean shift and particle filtering in a principled way,where a better proposal distribution is de-signed by first propagating particles via a motion model,and then running mean shift to move towards their local peaks in the likelihood image.An observation model in the particle filter incorporates global and local information within a region,and an adaptive motion model is adopted to depict the evolution of the object state.The algorithm needs fewer particles to manage the tracking task compared with the general particle filter,and recaptures the object quickly after occlusion occurs.Experiments on two image sequences demonstrate the effectiveness and robustness of the proposed algorithm.展开更多
To solve the problem that the accurate information of hand cannot be obtained by particle filter, a hand tracking algorithm based on particle filter combined with skin-color adaptive gradient vector flow(GVF) snake mo...To solve the problem that the accurate information of hand cannot be obtained by particle filter, a hand tracking algorithm based on particle filter combined with skin-color adaptive gradient vector flow(GVF) snake model is proposed. Adaptive GVF and skin color adaptive external guidance force are introduced to the traditional GVF snake model, guiding the curve to quickly converge to the deep concave region of hand contour and obtaining the complex hand contour accurately. This algorithm realizes a real-time correction of the particle filter parameters, avoiding the particle drift phenomenon. Experimental results show that the proposed algorithm can reduce the root mean square error of the hand tracking by 53%, and improve the accuracy of hand tracking in the case of complex and moving background, even with a large range of occlusion.展开更多
基金Supported by the National Natural Science Foundation of China （60634030）, the National Natural Science Foundation of China （60702066, 6097219） and the Natural Science Foundation of Henan Province （092300410158）.
文摘To solve the problem of strong nonlinear and motion model switching of maneuvering target tracking system in clutter environment, a novel maneuvering multi-target tracking algorithm based on multiple model particle filter is presented in this paper. The algorithm realizes dynamic combination of multiple model particle filter and joint probabilistic data association algorithm. The rapid expan- sion of computational complexity, caused by the simple combination of the interacting multiple model algorithm and particle filter is solved by introducing model information into the sampling process of particle state, and the effective validation and utilization of echo is accomplished by the joint proba- bilistic data association algorithm. The concrete steps of the algorithm are given, and the theory analysis and simulation results show the validity of the method.
基金Supported by the National Natural Science Foundation of China (No. 60672094)
文摘If a somewhat fast moving object exists in a complicated tracking environment, snake's nodes may fall into the inaccurate local minima. We propose a mean shift snake algorithm to solve this problem. However, if the object goes beyond the limits of mean shift snake module operation in suc- cessive sequences, mean shift snake's nodes may also fall into the local minima in their moving to the new object position. This paper presents a motion compensation strategy by using particle filter; therefore a new Particle Filter Mean Shift Snake (PFMSS) algorithm is proposed which combines particle filter with mean shift snake to fulfill the estimation of the fast moving object contour. Firstly, the fast moving object is tracked by particle filter to create a coarse position which is used to initialize the mean shift algorithm. Secondly, the whole relevant motion information is used to compensate the snake's node positions. Finally, snake algorithm is used to extract the exact object contour and the useful information of the object is fed back. Some real world sequences are tested and the results show that the novel tracking method have a good performance with high accuracy in solving the fast moving problems in cluttered background.
文摘To cope with the problem of tracking a human head in a complicated scene,we propose a method that adopts human skin color and hair color integrated with a kind of particle filter named condensation algorithm.Firstly,a novel method is presented to set up human head color model using skin color and hair color separately based on region growing.Compared with traditional human face model,this method is more precise and works well when human turns around and the face disappears in the image.Then a novel method is presented to use color model in condensation algorithm more effectively.In this method,a combination of edge detection result,color segmentation result and color edge detection result in an Omega window is used to measure the scale and position of human head in condensation.Experiments show that this approach can track human head in complicated scene even when human turns around or the distance of tracking a human head changes quickly.
文摘At present, the ballistic Target tracking has a higher demand in convergence rate and tracking precision of filter algorithm. In the paper, a filter algorithm was improved based on particle filter. The algorithm was carried out from the aspects such as particle degradation and particle diversity lack. A novel ballistic coefficient parameter model was built, and was expanded to the state vector for filtering. Finally, the improved algorithm was simulated by MATLAB software. The simulation results show that the algorithm can obtain better convergence speed and tracking precision.
基金Supported by the Significant Term of Science and Technology Research in Ministry of Education (No. 205060)Open Research Fund of National Mobile Communications Research Laboratory,Southeast University (N200911)+2 种基金Significant Basic Research of Jiangsu Province Colleges and Universities Natural Science Projects (07 KJA51006)Research Fund of Nanjing College of Traffic Vocational Technology (JY0903)Huawei Science and Technology Fund
文摘An adaptive object tracking algorithm based on particle filtering and a modified Gradient Vector Flow (GVF) Snake is proposed for tracking moving and deforming objects. The original contours of objects are obtained by using the background differencing method,and the true contours of objects can be converged by means of the powerful searching ability of a modified GVF-Snake. Finally,an Energetic Particle Filtering (EPF) algorithm is obtained by combining particle filtering and a modified GVF-Snake,and by using K-means and the EPF algorithm,multiple objects can be tracked. The proposed tracking tactic for partially occluded objects can effectively improve its anti-occlusion ability. Experiments show that this algorithm can obtain better tracking effect even though the tracked object is occluded.
基金Project(No.2006AA10Z204)supported by the National High-Tech Research and Development Program(863) of China
文摘We propose an algorithm of embedding ensemble tracking in a stochastic framework to achieve robust tracking performance under partial occlusion,illumination changes,and abrupt motion.It operates on likelihood images generated by the ensemble method,and combines mean shift and particle filtering in a principled way,where a better proposal distribution is de-signed by first propagating particles via a motion model,and then running mean shift to move towards their local peaks in the likelihood image.An observation model in the particle filter incorporates global and local information within a region,and an adaptive motion model is adopted to depict the evolution of the object state.The algorithm needs fewer particles to manage the tracking task compared with the general particle filter,and recaptures the object quickly after occlusion occurs.Experiments on two image sequences demonstrate the effectiveness and robustness of the proposed algorithm.
基金supported by the National Natural Sciencal Foundation of China(No.61403274)the Tianjin Technology Project of Intelligent Manufacturing(No.15ZXZNGX00160)
文摘To solve the problem that the accurate information of hand cannot be obtained by particle filter, a hand tracking algorithm based on particle filter combined with skin-color adaptive gradient vector flow(GVF) snake model is proposed. Adaptive GVF and skin color adaptive external guidance force are introduced to the traditional GVF snake model, guiding the curve to quickly converge to the deep concave region of hand contour and obtaining the complex hand contour accurately. This algorithm realizes a real-time correction of the particle filter parameters, avoiding the particle drift phenomenon. Experimental results show that the proposed algorithm can reduce the root mean square error of the hand tracking by 53%, and improve the accuracy of hand tracking in the case of complex and moving background, even with a large range of occlusion.
