Zero-power communication, enabled by energy harvesting, backscattering, and low-power computing, is capable of fulfilling the requirements of emerging Internet of Things (IoT) communication scenarios that demand low cost, compact size, and minimal power consumption. Thus, it holds great potential as a transformative technology for the future of IoT. Trusted access and secure transmission remain essential in zero-power communication scenarios. Nevertheless, conventional complex security mechanisms become impractical due to limited power consumption and resources. This work presents a lightweight security protocol for authentication. Initially, a sliding window algorithm, utilizing the Hamming distance, is designed to generate the message digest. This algorithm leverages the remaining electric quantity of the transmitter as a secret parameter for authentication. Subsequently, a key distribution function based on the hash chain is employed to ensure the security of the session key. The protocol’s security attributes regarding transmitted data and its ability to withstand common attacks are demonstrated through formal security analysis and the utilization of the ProVerif analysis tool. Extensive simulations validate the efficacy of the proposed security algorithms, which are well suited for lightweight IoT devices with severely constrained resources and outperform benchmark algorithms.

Forecasting the future trajectory of pedestrians is an important task in computer vision with a range of applications, from security cameras to autonomous driving. It is very challenging because pedestrians not only move individually across time but also interact spatially, and the spatial and temporal information is deeply coupled with one another in a multi-agent scenario. Learning such complex spatio-temporal correlation is a fundamental issue in pedestrian trajectory prediction. Inspired by the procedure that the hippocampus processes and integrates spatio-temporal information to form memories, we propose a novel multi-stream representation learning module to learn complex spatio-temporal features of pedestrian trajectory. Specifically, we learn temporal, spatial and cross spatio-temporal correlation features in three respective pathways and then adaptively integrate these features with learnable weights by a gated network. Besides, we leverage the sparse attention gate to select informative interactions and correlations brought by complex spatio-temporal modeling and reduce complexity of our model. We evaluate our proposed method on two commonly used datasets, i.e. ETH-UCY and SDD, and the experimental results demonstrate our method achieves the state-of-the-art performance. Code: https://github.com/YuxuanIAIR/MSRL-master

The main challenge of Multi-Object Tracking (MOT) lies in maintaining a continuous trajectory for each target. Existing methods often learn reliable motion patterns to match the same target between adjacent frames and discriminative appearance features to re-identify the lost targets after a long period. However, the reliability of motion prediction and the discriminability of appearances can be easily hurt by dense crowds and extreme occlusions in the tracking process. In this paper, we propose a simple yet effective multi-object tracker, i.e., MotionTrack, which learns robust short-term and long-term motions in a unified framework to associate trajectories from a short to long range. For dense crowds, we design a novel Interaction Module to learn interaction-aware motions from short-term trajectories, which can estimate the complex movement of each target. For extreme occlusions, we build a novel Refind Module to learn reliable long-term motions from the target's history trajectory, which can link the interrupted trajectory with its corresponding detection. Our Interaction Module and Refind Module are embedded in the well-known tracking-by-detection paradigm, which can work in tandem to maintain superior performance. Extensive experimental results on MOT17 and MOT20 datasets demonstrate the superiority of our approach in challenging scenarios, and it achieves state-of-the-art performances at various MOT metrics. Code is available at https://github.com/qwomeng/MotionTrack.

Forecasting multiple pedestrian trajectories is a challenging task for real-world applications, as the motion patterns of pedestrian are essentially stochastic and uncertain. Previous works have demonstrated that predicting diverse goals in advance can effectively improve the performance of pedestrian trajectory prediction. However, these methods are either unable to perform probabilistic and high-efficiency trajectory prediction, or mainly rely on the predefined template trajectories which are not high-performance and insufficient to represent the possible pedestrian behaviors. In this paper, we propose a new Probabilistic Proposal Network (PPNet) to concentrate on the generation of goals and the utilization of goal guidance. PPNet firstly generates multiple weighted goals based on the diverse latent intentions automatically obtained by unsupervised learning, and then designs the goal-conditioned Transformer networks to predict probabilistic proposals as the final trajectories. Extensive experimental results on ETH/UCY datasets and Stanford Drone Dataset indicate that PPNet achieves both state-of-the-art performance and high efficiency on pedestrian trajectory prediction.

Pedestrian trajectory prediction is crucial in many practical applications due to the diversity of pedestrian movements, such as social interactions and individual motion behaviors. With similar observable trajectories and social environments, different pedestrians may make completely different future decisions. However, most existing methods only focus on the frequent modal of the trajectory and thus are difficult to generalize to the peculiar scenario, which leads to the decline of the multimodal fitting ability when facing similar scenarios. In this paper, we propose a complementary attention gated network (CAGN) for pedestrian trajectory prediction, in which a dual-path architecture including normal and inverse attention is proposed to capture both frequent and peculiar modals in spatial and temporal patterns, respectively. Specifically, a complementary block is proposed to guide normal and inverse attention, which are then be summed with learnable weights to get attention features by a gated network. Finally, multiple trajectory distributions are estimated based on the fused spatio-temporal attention features due to the multimodality of future trajectory. Experimental results on benchmark datasets, i.e., the ETH, and the UCY, demonstrate that our method outperforms state-of-the-art methods by 13.8% in Average Displacement Error (ADE) and 10.4% in Final Displacement Error (FDE). Code will be available at https://github.com/jinghaiD/CAGN