Traffic anomaly detection (TAD) in driving videos is critical for ensuring the safety of autonomous driving and advanced driver assistance systems. Previous single-stage TAD methods primarily rely on frame prediction, making them vulnerable to interference from dynamic backgrounds induced by the rapid movement of the dashboard camera. While two-stage TAD methods appear to be a natural solution to mitigate such interference by pre-extracting background-independent features (such as bounding boxes and optical flow) using perceptual algorithms, they are susceptible to the performance of first-stage perceptual algorithms and may result in error propagation. In this paper, we introduce TTHF, a novel single-stage method aligning video clips with text prompts, offering a new perspective on traffic anomaly detection. Unlike previous approaches, the supervised signal of our method is derived from languages rather than orthogonal one-hot vectors, providing a more comprehensive representation. Further, concerning visual representation, we propose to model the high frequency of driving videos in the temporal domain. This modeling captures the dynamic changes of driving scenes, enhances the perception of driving behavior, and significantly improves the detection of traffic anomalies. In addition, to better perceive various types of traffic anomalies, we carefully design an attentive anomaly focusing mechanism that visually and linguistically guides the model to adaptively focus on the visual context of interest, thereby facilitating the detection of traffic anomalies. It is shown that our proposed TTHF achieves promising performance, outperforming state-of-the-art competitors by +5.4% AUC on the DoTA dataset and achieving high generalization on the DADA dataset.

The pedestrian trajectory prediction task is an essential component of intelligent systems. Its applications include but are not limited to autonomous driving, robot navigation, and anomaly detection of monitoring systems. Due to the diversity of motion behaviors and the complex social interactions among pedestrians, accurately forecasting their future trajectory is challenging. Existing approaches commonly adopt generative adversarial networks (GANs) or conditional variational autoencoders (CVAEs) to generate diverse trajectories. However, GAN-based methods do not directly model data in a latent space, which may make them fail to have full support over the underlying data distribution. CVAE-based methods optimize a lower bound on the log-likelihood of observations, which may cause the learned distribution to deviate from the underlying distribution. The above limitations make existing approaches often generate highly biased or inaccurate trajectories. In this article, we propose a novel generative flow-based framework with a dual-graphormer for pedestrian trajectory prediction (STGlow). Different from previous approaches, our method can more precisely model the underlying data distribution by optimizing the exact log-likelihood of motion behaviors. Besides, our method has clear physical meanings for simulating the evolution of human motion behaviors. The forward process of the flow gradually degrades complex motion behavior into simple behavior, while its reverse process represents the evolution of simple behavior into complex motion behavior. Furthermore, we introduce a dual-graphormer combined with the graph structure to more adequately model the temporal dependencies and the mutual spatial interactions. Experimental results on several benchmarks demonstrate that our method achieves much better performance compared to previous state-of-the-art approaches.

Image sharing chain detection aims to recover the sharing history of an image downloaded from online social networks (OSNs), including the ever-shared OSNs and their orders, which is an important task in the multimedia forensics community. Most of the existing algorithms directly treat the sharing chain detection as a classification problem by simply assigning a unique label to each sharing chain. Such a strategy though seems straightforward, it ignores the inherent properties of the sharing chain which can be regarded as a time sequence that carries the sharing history of an online image. In this paper, we suggest a new sharing chain detection framework via Sequence-to-Sequence (Seq2Seq) model. Different from previous classification based approaches, our model detects the sharing chain of online image progressively via a decoder. This progressive manner can fully utilize the decoded chain, which is embedded into a series of learned representations. Experimental results show that our method can detect sharing chains involving up to three OSNs, and exhibits much better performance than conventional ones.

Image restoration is a fundamental task in low-level computer vision. Most existing algorithms assume that the input image has a single known degradation type. In reality, images usually contain multiple degradations, making the restoration challenging. Though recent works restore the multiple degraded images, they assume that the degradation history is known. Obviously, such an ideal assumption often does not hold in real applications. This work proposes a novel restoration framework for multiple degraded images via degradation history estimation. Specifically, we first develop a sequential model to estimate the degradation history, including both the degradation operation chain and the corresponding parameters. By resorting to designed self-attention and cross-attention mechanisms, our method can effectively model the correlation of the input image, degradation operation chain, and parameters. Then, we apply our estimation framework for the multiple degraded image restoration, without requiring the degradation history. Experiment results demonstrate much better performance than existing approaches.

Predicting the motion trajectories of moving agents in complex traffic scenes, such as crossroads and roundabouts, plays an important role in cooperative intelligent transportation systems. Nevertheless, accurately forecasting the motion behavior in a dynamic scenario is challenging due to the complex cooperative interactions between moving agents. Graph Convolutional Neural Network has recently been employed to deal with the cooperative interactions between agents. Despite the promising performance of resulting trajectory prediction algorithms, many existing graph-based approaches model interactions with an undirected graph, where the strength of influence between agents is assumed to be symmetric. However, such an assumption often does not hold in reality. For example, in pedestrian or vehicle interaction modeling, the moving behavior of a pedestrian or vehicle is highly affected by the ones ahead, while the ones ahead usually pay less attention to the ones behind. To fully exploit the asymmetric attributes of the cooperative interactions in intelligent transportation systems, in this work, we present a directed graph convolutional neural network for multiple agents trajectory prediction. First, we propose three directed graph topologies, i.e., view graph, direction graph, and rate graph, by encoding different prior knowledge of a cooperative scenario, which endows the capability of our framework to effectively characterize the asymmetric influence between agents. Then, a fusion mechanism is devised to jointly exploit the asymmetric mutual relationships embedded in constructed graphs. Furthermore, a loss function based on Cauchy distribution is designed to generate multimodal trajectories. Experimental results on complex traffic scenes demonstrate the superior performance of our proposed model when compared with existing approaches.

Predicting human motion behavior in a crowd is important for many applications, ranging from the natural navigation of autonomous vehicles to intelligent security systems of video surveillance. All the previous works model and predict the trajectory with a single resolution, which is relatively ineffective and difficult to simultaneously exploit the long-range information (e.g., the destination of the trajectory), and the short-range information (e.g., the walking direction and speed at a certain time) of the motion behavior. In this paper, we propose a temporal pyramid network for pedestrian trajectory prediction through a squeeze modulation and a dilation modulation. Our hierarchical framework builds a feature pyramid with increasingly richer temporal information from top to bottom, which can better capture the motion behavior at various tempos. Furthermore, we propose a coarse-to-fine fusion strategy with multi-supervision. By progressively merging the top coarse features of global context to the bottom fine features of rich local context, our method can fully exploit both the long-range and short-range information of the trajectory. Experimental results on two benchmarks demonstrate the superiority of our method. Our code and models will be available upon acceptance.

Two-player zero-sum video game is a basic and important problem in game artificial intelligence. In 2020, enhanced rolling horizon evolution algorithm with policy gradient (ERHEAPI) beat heuristics, Monte-Carlo tree search and other methods to win the championship of Fighting Game Artificial Intelligence Competition (FTGAIC). However, the performance of ERHEAPI in the first round was not good. In this paper, we present an effective method noted as ERHEAPPO that combines proximal policy optimization (PPO) and enhanced rolling horizon evolution algorithm (ERHEA) with opponent model learning to further improve performance. We train the PPO agent and find that the Elo-based opponent selection can improve the sample efficiency. We compare the performance of the proposed ERHEAPPO with ERHEAPI. The experimental results demonstrate the effectiveness of ERHEAPPO.