A Real-Time Multi-Vehicle Tracking Framework in Intelligent Vehicular Networks

In this paper,we provide a new approach for intelligent traffic transportation in the intelligent vehicular networks,which aims at collecting the vehicles’locations,trajectories and other key driving parameters for the time-critical autonomous driving’s requirement.The key of our method is a multi-vehicle tracking framework in the traffic monitoring scenario..Our proposed framework is composed of three modules:multi-vehicle detection,multi-vehicle association and miss-detected vehicle tracking.For the first module,we integrate self-attention mechanism into detector of using key point estimation for better detection effect.For the second module,we apply the multi-dimensional information for robustness promotion,including vehicle re-identification(Re-ID)features,historical trajectory information,and spatial position information For the third module,we re-track the miss-detected vehicles with occlusions in the first detection module.Besides,we utilize the asymmetric convolution and depth-wise separable convolution to reduce the model’s parameters for speed-up.Extensive experimental results show the effectiveness of our proposed multi-vehicle tracking framework.

一种新型群智感知系统架构模型和实现方法

物联网移动群智感知模式本质是汇聚大规模普通移动个体的智能感知能力,对开放、动态、复杂物理环境进行感知,通过感知大数据的智能分析,对感知群体引导和反馈,使其持续涌现群体智能并辅助综合决策.目前的群智感知系统仍然存在个体感知欠智能、群体目标少引导、群智过程弱调控三方面的局限性.本文首先讨论了群智感知研究现状和面临的挑战;结合人机物融合、云边端协同、感算控闭环3个核心发展趋势,提出一种智能演进与反馈引导结合的新型群智感知系统架构模型Evo-CPS,并研究了该模型的实现方法;然后结合智慧出行应用场景,将所提出的理论方法进行应用验证;最后,总结全文并展望新一代群智感知研究领域的发展方向.

See clearly on rainy days:Hybrid multiscale loss guided multifeature fusion network for single image rain removal

The quality of photos is highly susceptible to severe weather such as heavy rain;it can also degrade the performance of various visual tasks like object detection.Rain removal is a challenging problem because rain streaks have different appearances even in one image.Regions where rain accumulates appear foggy or misty,while rain streaks can be clearly seen in areas where rain is less heavy.We propose removing various rain effects in pictures using a hybrid multiscale loss guided multiple feature fusion de-raining network(MSGMFFNet).Specially,to deal with rain streaks,our method generates a rain streak attention map,while preprocessing uses gamma correction and contrast enhancement to enhanced images to address the problem of rain accumulation.Using these tools,the model can restore a result with abundant details.Furthermore,a hybrid multiscale loss combining L1 loss and edge loss is used to guide the training process to pay attention to edge and content information.Comprehensive experiments conducted on both synthetic and real-world datasets demonstrate the effectiveness of our method.

ASAR: An ant-based service-aware routing algorithm for multimedia sensor networks

Aimed at three basic services(event-driven,data query and stream query),the paper presents a QoS routing model for multimedia sensor networks.Moreover,based on the traditional ant-based algorithm,we propose an ant-based service-aware routing(ASAR)algorithm.The ASAR chooses suitable paths to meet diverse QoS requirements from different kinds of services,thus maximizing network utilization and improving network performance.Finally,extensive simulation is conducted to verify the effectiveness of our solution and we give a detailed discussion on the effects of different system parameters.Compared to the typical routing algorithm in sensor networks and the traditional ant-based algorithm,our ASAR algorithm has better convergence and significantly provides better QoS for multiple types of services in the multimedia sensor networks.

StreamTune： dynamic resource scheduling approach for workload skew in video data center

Video surveillance applications need video data center to provide elastic virtual machine （VM） provisioning. However, the workloads of the VMs are hardly to be predicted for online video surveillance service. The unknown arrival workloads easily lead to workload skew among VMs. In this paper, we study how to balance the workload skew on online video surveillance system. First, we design the system framework for online surveillance service which con- sists of video capturing and analysis tasks. Second, we propose StreamTune, an online resource scheduling approach for workload balancing, to deal with irregular video analysis workload with the minimum number of VMs. We aim at timely balancing the workload skew on video analyzers without depending on any workload prediction method. Furthermore, we evaluate the performance of the proposed approach using a traffic surveillance application. The experimental results show that our approach is well adaptive to the variation of workload and achieves workload balance with less VMs.

An end-to-end convolutional network for joint detecting and denoising adversarial perturbations in vehicle classification

Deep convolutional neural networks(DCNNs)have been widely deployed in real-world scenarios.However,DCNNs are easily tricked by adversarial examples,which present challenges for critical applications,such as vehicle classification.To address this problem,we propose a novel end-to-end convolutional network for joint detection and removal of adversarial perturbations by denoising(DDAP).It gets rid of adversarial perturbations using the DDAP denoiser based on adversarial examples discovered by the DDAP detector.The proposed method can be regarded as a pre-processing step—it does not require modifying the structure of the vehicle classification model and hardly affects the classification results on clean images.We consider four kinds of adversarial attack(FGSM,BIM,DeepFool,PGD)to verify DDAP’s capabilities when trained on BIT-Vehicle and other public datasets.It provides better defense than other state-of-the-art defensive methods.