The problem of recognizing natural scenes, such as water, smoke, fire, wind-blown vegetation and a flock of flying birds, is considered. These scenes exhibit the characteristic dynamic pattern, but have stochastic ext...The problem of recognizing natural scenes, such as water, smoke, fire, wind-blown vegetation and a flock of flying birds, is considered. These scenes exhibit the characteristic dynamic pattern, but have stochastic extent. They are referred to as dynamic texture（DT）. In reality, the diversity of DTs on different viewpoints and scales are very common, which also bring great difficulty to recognize DTs. In the previous studies, due to no considering of the deformable and transient nature of elements in DT, the motion estimation method is based on brightness constancy assumption,which seem inappropriate for aggregate and complex motions. A novel motion model based on relative motion in the neighborhood of two-dimensional motion fields is proposed. The estimation of non-rigid motion of DTs is based on the continuity equation, and then the local vector difference（LVD） is proposed to characterize DT local relative motion. Spatiotemporal statistics of the LVDs is used as the representation of DT sequences. Excellent performances of classifying all DTs in UCLA database demonstrate the capability of the proposed method in describing DT.展开更多
The wireless visual sensor network(WVSN)as a new emerged intelligent visual system,has been applied in many video monitoring sites.However,there is still great challenge because of the limited wireless network bandwid...The wireless visual sensor network(WVSN)as a new emerged intelligent visual system,has been applied in many video monitoring sites.However,there is still great challenge because of the limited wireless network bandwidth.To resolve the problem,we propose a real-time dynamic texture approach which can detect and reduce the temporal redundancy during many successive image frames.Firstly,an adaptively learning background model is improved to discover successive similar image frames from the inputting video sequence.Then,the dynamic texture model based on the singular value decomposition is adopted to distinguish foreground and background element dynamics.Furthermore,a background discarding strategy based on visual motion coherence is proposed to determine whether each image frame is streamed or not.To evaluate the trade-off performance of the proposed method,it is tested on the CDW-2014 dataset,which can accurately detect the first foreground frame when the moving objects of interest appear in the field of view in the most tested dynamic scenes,and the misdetection rate of the undetected foreground frames is near to zero.Compared to the original stream,it can reduce the occupied bandwidth a lot and its computational cost is relatively lower than the state-of-the-art methods.展开更多
基金supported by the National Natural Science Foundation of China(41504115)the Shaanxi Province Natural Science Foundation(2015JQ6223)+2 种基金the Foundation of Strengthening Police Science and Technology from Ministry of Public Security(2015GABJC50)the International Technology Cooperation Plan Project of Shaanxi Province(2015KW-0142015KW-013)
文摘The problem of recognizing natural scenes, such as water, smoke, fire, wind-blown vegetation and a flock of flying birds, is considered. These scenes exhibit the characteristic dynamic pattern, but have stochastic extent. They are referred to as dynamic texture（DT）. In reality, the diversity of DTs on different viewpoints and scales are very common, which also bring great difficulty to recognize DTs. In the previous studies, due to no considering of the deformable and transient nature of elements in DT, the motion estimation method is based on brightness constancy assumption,which seem inappropriate for aggregate and complex motions. A novel motion model based on relative motion in the neighborhood of two-dimensional motion fields is proposed. The estimation of non-rigid motion of DTs is based on the continuity equation, and then the local vector difference（LVD） is proposed to characterize DT local relative motion. Spatiotemporal statistics of the LVDs is used as the representation of DT sequences. Excellent performances of classifying all DTs in UCLA database demonstrate the capability of the proposed method in describing DT.
基金the Science and Technology Research Program of Hubei Provincial Department of Education(No.T201805)the PhD Research Startup Foundation of Hubei University of Technology(No.BSQD13032)。
文摘The wireless visual sensor network(WVSN)as a new emerged intelligent visual system,has been applied in many video monitoring sites.However,there is still great challenge because of the limited wireless network bandwidth.To resolve the problem,we propose a real-time dynamic texture approach which can detect and reduce the temporal redundancy during many successive image frames.Firstly,an adaptively learning background model is improved to discover successive similar image frames from the inputting video sequence.Then,the dynamic texture model based on the singular value decomposition is adopted to distinguish foreground and background element dynamics.Furthermore,a background discarding strategy based on visual motion coherence is proposed to determine whether each image frame is streamed or not.To evaluate the trade-off performance of the proposed method,it is tested on the CDW-2014 dataset,which can accurately detect the first foreground frame when the moving objects of interest appear in the field of view in the most tested dynamic scenes,and the misdetection rate of the undetected foreground frames is near to zero.Compared to the original stream,it can reduce the occupied bandwidth a lot and its computational cost is relatively lower than the state-of-the-art methods.
