A solution is proposed for the real-time vehicle verification which is an important problem for numerous on- road vehicle applications. First, based on the vertical symmetry characteristics of vehicle images, a vertic...A solution is proposed for the real-time vehicle verification which is an important problem for numerous on- road vehicle applications. First, based on the vertical symmetry characteristics of vehicle images, a vertical symmetrical histograms of oriented gradients （VS-HOG） descriptor is proposed for extracting the image features. In the classification stage, an extreme learning machine （ELM） is used to improve the real-time performance. Experimental data demonstrate that, compared with other classical methods, the vehicle verification algorithm based on VS-HOG and ELM achieves a better trade-off between cost and performance. The computational cost is reduced by using the algorithm, while keeping the performance loss as low as possible. Furthermore, experimental results further show that the proposed vehicle verification method is suitable for on-road vehicle applications due to its better performance both in efficiency and accuracy.展开更多
To better understand characteristics of seismic signals of tracked vehicles measured when passing a sensor line,we numerically modelled force-pulse responses of a layered soil that is similar in its seismic properties...To better understand characteristics of seismic signals of tracked vehicles measured when passing a sensor line,we numerically modelled force-pulse responses of a layered soil that is similar in its seismic properties to that found at the original measurement site.The vertical-force pulses from the road wheels rolling over the track elements are fitted to the measured ones.Single-pulse seismic waves vary with distance due to diff erent wave types,refl ections at layer boundaries,vehicle velocity and relative position of the left and right track elements.They are computed by a modelling program and superposed at sensor positions with the appropriate slant distance and time shift for each track element.These sum signals are in qualitative agreement with those from the original measurements.However,they are several magnitudes weaker and much smoother.Furthermore,higher frequencies are damped much less at larger distances.Due to the large variability of the sum signals,recognition of tracked-vehicle types exclusively through their seismic signals seems diffi cult.展开更多
基金The National Natural Science Foundation of China(No.61203237)the Natural Science Foundation of Zhejiang Province(No.LQ12F03016)the China Postdoctoral Science Foundation(No.2011M500836)
文摘A solution is proposed for the real-time vehicle verification which is an important problem for numerous on- road vehicle applications. First, based on the vertical symmetry characteristics of vehicle images, a vertical symmetrical histograms of oriented gradients （VS-HOG） descriptor is proposed for extracting the image features. In the classification stage, an extreme learning machine （ELM） is used to improve the real-time performance. Experimental data demonstrate that, compared with other classical methods, the vehicle verification algorithm based on VS-HOG and ELM achieves a better trade-off between cost and performance. The computational cost is reduced by using the algorithm, while keeping the performance loss as low as possible. Furthermore, experimental results further show that the proposed vehicle verification method is suitable for on-road vehicle applications due to its better performance both in efficiency and accuracy.
文摘To better understand characteristics of seismic signals of tracked vehicles measured when passing a sensor line,we numerically modelled force-pulse responses of a layered soil that is similar in its seismic properties to that found at the original measurement site.The vertical-force pulses from the road wheels rolling over the track elements are fitted to the measured ones.Single-pulse seismic waves vary with distance due to diff erent wave types,refl ections at layer boundaries,vehicle velocity and relative position of the left and right track elements.They are computed by a modelling program and superposed at sensor positions with the appropriate slant distance and time shift for each track element.These sum signals are in qualitative agreement with those from the original measurements.However,they are several magnitudes weaker and much smoother.Furthermore,higher frequencies are damped much less at larger distances.Due to the large variability of the sum signals,recognition of tracked-vehicle types exclusively through their seismic signals seems diffi cult.
