Transformer-based multiobject tracking (MOT) methods have attracted significant attention from researchers. However, these Transformer-based models often suffer from suboptimal inference speeds due to their architectural complexities or other inherent issues, rendering them difficult to deploy in practical industrial applications. To address this challenge, we revisited the classic joint detection and tracking (JDT) paradigm and analyzed existing models. Drawing inspiration from Detection Transformer’s (DETR) object queries, which naturally encode object appearance features, we constructed a fast and novel JDT-type MOT framework named FastTrackTr by implementing an efficient interframe information transfer mechanism. This framework integrates three key technological innovations: a cross-decoder mechanism that implicitly incorporates historical trajectory information without requiring additional queries or decoders, a historical encoder and decoder pair for refining and utilizing historical feature representations, and a deterministic fixed-shape architecture that enables seamless TensorRT acceleration. Benefiting from these designs, our approach not only reduces the number of queries required for tracking but also avoids introducing excessive network structures, ensuring model simplicity while maintaining high accuracy. Experimental results show that our method achieves real-time tracking while maintaining state-of-the-art accuracy. On an NVIDIA RTX 4090 with an image size of 1333 × 800, it reaches 62.4 higher order tracking accuracy (HOTA) at 86.6 frames per second (FPS) on the DanceTrack dataset, outperforming other advanced Transformer-based methods. Furthermore, it excels on edge devices, such as the NVIDIA Jetson AGX Orin, where its lightweight variant achieves up to 59.2 FPS on 640 × 640 images, meeting real-time requirements for practical applications.

Medial Axis Transform (MAT) has been recently adopted as the acceleration structure of broad-phase collision detection. Compared to traditional BVH-based methods, MAT can provide a high-fidelity volumetric approximation of 3D complex objects, resulting in higher collision culling efficiency. However, due to MAT’s non-hierarchical structure, it may be outperformed in collision-light scenarios because several cullings at the top level of a BVH may take a large number of cullings with MAT. We propose a collision detection method that combines MAT and BVH to address the above problem. Our technique efficiently culls collisions between dynamic and static objects. Experimental results show that our method has higher culling efficiency than pure BVH or MAT methods.

Summary Securely performing nearest neighbor query over encrypted data in cloud is an important topic in the area of cloud computing, for which Wang et al recently put forward a scheme (ie, CloudBI‐II) to address the challenging security problem: resisting the collusion of cloud server and query users. In this paper, we propose an efficient attack method that indicates CloudBI‐II will reveal the difference vectors under the collusion attack. Furthermore, we show that the difference vector disclosure will result in serious privacy breach and, thus, attain an efficient attack method to break CloudBI‐II. Namely, CloudBI‐II cannot achieve their declared security. Through theoretical analysis and experiment evaluation, we confirm that our proposed attack approach can fast recover the original data from the encrypted data set in CloudBI‐II. Finally, we provide an enhanced scheme that can efficiently resist the collusion attack.

Recently, Rahulamathavan et al. propose a privacy preserving scheme for outsourcing SVM classification. Their core contribution is a secure protocol to attain the sign of numbers in encrypted form. In this paper, we observe that Rahulamathavan et al.'s protocol will suffer from some soundness and security problems. Then, we propose a new scheme to securely obtain the encrypted numbers' sign. Theoretical analysis and experiment results show our proposed scheme can not only fix the soundness and security problems, but also achieve higher efficiency.

Privacy-preserving distributed set intersection and union have been widely applied in many scenarios and lots of work has paid attention to the problem. Existing solutions to privacy-preserving set intersection and union are built on secure multiparty computation protocols, which can theoretically solve it, but result in heavy computation and communication overhead. Worse still, most of the existing schemes cannot work once some participant fails. In this paper, we propose two differentially private approaches for distributed set intersection and union, respectively. In our schemes, each data contributor possesses a secret data set and perturbs it by randomized response technique to satisfy local differential privacy. Then the collector gathers all contributors' perturbed data sets and utilizes maximum likelihood estimation to gain an accurate estimation of intersection and union. Compared to existing schemes, the proposed schemes can dramatically reduce computation and communication overhead, and tolerate participant's failure. We formally prove that the proposed schemes satisfy local differential privacy, and leverage extensive experiments to evaluate the proposed approaches. The results indicate that our schemes have low computation and communication complexity, strong robustness and good utility.