In recent years, the number of traffic accident deaths due to distracted driving has been increasing dramatically. Fortunately, distracted driving can be detected by the rapidly developing deep learning technology. Nevertheless, considering that real-time detection is necessary, three contradictory requirements for an optimized network must be addressed: a small number of parameters, high accuracy, and high speed. We propose a new D-HCNN model based on a decreasing filter size with only 0.76M parameters, a much smaller number of parameters than that used by models in many other studies. D-HCNN uses HOG feature images, L2 weight regularization, dropout and batch normalization to improve the performance. We discuss the advantages and principles of D-HCNN in detail and conduct experimental evaluations on two public datasets, AUC Distracted Driver (AUCD2) and State Farm Distracted Driver Detection (SFD3). The accuracy on AUCD2 and SFD3 is 95.59% and 99.87%, respectively, higher than the accuracy achieved by many other state-of-the-art methods.

Since underwater acoustic channels are shared by multiple heterogeneous entities and can suffer from severe interference, underwater acoustic communication networks (UACNs) are faced with the challenge of mitigating interference and improving communication quality by implementing distributed resource allocation approaches. In this work, we introduce the concept of reinforced learning in intelligent control to the UACNs by treating the nodes as intelligent agents and the node networks as multi-agent networks. By partitioning the state space and the action space, we formulate a reward function and a search strategy and propose a distributed resource allocation algorithm based on cooperative Q-Learning. In addition, we verify the convergence of the proposed algorithm. Finally, simulation results in two different underwater application scenarios show that the proposed algorithm outperforms the existing algorithms in improving the network transmission capacity, and can reduce the overhead of resource allocation by using cooperative Q-Learning.

Unsupervised video anomaly detection (UVAD) has gained significant attention due to its label-free nature. Typically, UVAD methods can be categorized into two branches, i.e. the one-class classification (OCC) methods and fully UVAD ones. However, the former may suffer from data imbalance and high false alarm rates, while the latter relies heavily on feature representation and pseudo-labels. In this paper, a novel feature reconstruction and disruption model (FRD-UVAD) is proposed for effective feature refinement and better pseudo-label generation in fully UVAD, based on cascade cross-attention transformers, a latent anomaly memory bank and an auxiliary scorer. The clip features are reconstructed using the space-time intra-clip information, as well as cross-inter-clip knowledge. Moreover, instead of blindly reconstructing all training features as OCC methods, a new disruption process is proposed to cooperate with the feature reconstruction simultaneously. Using the collected pseudo anomaly samples, it is able to emphasize the feature differences between normal and abnormal events. Additionally, a pre-trained UVAD scorer is utilized as a different criteria for anomaly prediction, which further refines the pseudo-labels. To demonstrate its effectiveness, comprehensive experiments and detailed ablation studies are conducted on three video benchmarks, namely CUHK Avenue, ShanghaiTech and UCF-Crime. Our proposed model (FRD-UVAD) achieves the best AUC performance (91.23%, 80.14%, and 82.12%) on all three datasets, surpassing other state-of-the-art OCC and fully UVAD methods. Furthermore, it obtains the lowest false alarm rate with a lower scene dependency, compared with other OCC methods. The code is available at https://github.com/tcc-power/FRD-unsupervised-video-anomaly-detection .