Millimeter wave(mm Wave) has been claimed as the viable solution for high-bandwidth vehicular communications in 5 G and beyond. To realize applications in future vehicular communications, it is important to take a rob...Millimeter wave(mm Wave) has been claimed as the viable solution for high-bandwidth vehicular communications in 5 G and beyond. To realize applications in future vehicular communications, it is important to take a robust mm Wave vehicular network into consideration. However, one challenge in such a network is that mm Wave should provide an ultra-fast and high-rate data exchange among vehicles or vehicle-to-infrastructure(V2 I). Moreover,traditional real-time channel estimation strategies are unavailable because vehicle mobility leads to a fast variation mm Wave channel. To overcome these issues, a channel estimation approach for mm Wave V2 I communications is proposed in this paper. Specifically, by considering a fast-moving vehicle secnario, a corresponding mathematical model for a fast time-varying channel is first established. Then, the temporal variation rule between the base station and each mobile user and the determined direction-of-arrival are used to predict the time-varying channel prior information(PI). Finally, by exploiting the PI and the characteristics of the channel, the time-varying channel is estimated. The simulation results show that the scheme in this paper outperforms traditional ones in both normalized mean square error and sum-rate performance in the mm Wave time-varying vehicular system.展开更多
With complementary multi-modal information(i.e. visible and thermal), multispectral pedestrian detection is essential for around-the-clock applications, such as autonomous driving, video surveillance, and vicinagearth...With complementary multi-modal information(i.e. visible and thermal), multispectral pedestrian detection is essential for around-the-clock applications, such as autonomous driving, video surveillance, and vicinagearth security. Despite its broad applications, the requirements for expensive thermal device and multi-sensor alignment limit the utilization in real-world applications. In this paper, we propose a pseudo-multispectral pedestrian detection(called Pseudo MPD) method,which employs the gray image converted from the RGB image to replace the real thermal image,and learns the pseudo-thermal feature through deep thermal feature guidance(TFG). To achieve this goal, we first introduce an image base-detail decomposition(IBD) module to decompose image information into base and detail parts. Afterwards, we design a base-detail hierarchical feature fusion(BHFF) module to deeply exploit the information between these two parts, and employ a TFG module to guide pseudo-thermal base and detail feature learning. As a result, our proposed method does not require the real thermal image during inference. The comprehensive experiments are performed on two public multispectral pedestrian datasets. The experimental results demonstrate the effectiveness of our proposed method.展开更多
基金Project supported by the National Natural Science Foundation of China (No. 61971063)。
文摘Millimeter wave(mm Wave) has been claimed as the viable solution for high-bandwidth vehicular communications in 5 G and beyond. To realize applications in future vehicular communications, it is important to take a robust mm Wave vehicular network into consideration. However, one challenge in such a network is that mm Wave should provide an ultra-fast and high-rate data exchange among vehicles or vehicle-to-infrastructure(V2 I). Moreover,traditional real-time channel estimation strategies are unavailable because vehicle mobility leads to a fast variation mm Wave channel. To overcome these issues, a channel estimation approach for mm Wave V2 I communications is proposed in this paper. Specifically, by considering a fast-moving vehicle secnario, a corresponding mathematical model for a fast time-varying channel is first established. Then, the temporal variation rule between the base station and each mobile user and the determined direction-of-arrival are used to predict the time-varying channel prior information(PI). Finally, by exploiting the PI and the characteristics of the channel, the time-varying channel is estimated. The simulation results show that the scheme in this paper outperforms traditional ones in both normalized mean square error and sum-rate performance in the mm Wave time-varying vehicular system.
基金supported by the National Key Research and Development Program of China (Grant No. 2022ZD0160400)the National Natural Science Foundation of China (Grant No. 62106152)
文摘With complementary multi-modal information(i.e. visible and thermal), multispectral pedestrian detection is essential for around-the-clock applications, such as autonomous driving, video surveillance, and vicinagearth security. Despite its broad applications, the requirements for expensive thermal device and multi-sensor alignment limit the utilization in real-world applications. In this paper, we propose a pseudo-multispectral pedestrian detection(called Pseudo MPD) method,which employs the gray image converted from the RGB image to replace the real thermal image,and learns the pseudo-thermal feature through deep thermal feature guidance(TFG). To achieve this goal, we first introduce an image base-detail decomposition(IBD) module to decompose image information into base and detail parts. Afterwards, we design a base-detail hierarchical feature fusion(BHFF) module to deeply exploit the information between these two parts, and employ a TFG module to guide pseudo-thermal base and detail feature learning. As a result, our proposed method does not require the real thermal image during inference. The comprehensive experiments are performed on two public multispectral pedestrian datasets. The experimental results demonstrate the effectiveness of our proposed method.
