Abstract Many Multi-Object Tracking (MOT) approaches exploit motion information to associate all the detected objects across frames. However, traditional tracking-by-detection (TBD) methods, relying on the Kalman Filter, often work well in linear motion scenarios but struggle to accurately predict the locations of objects undergoing complex and non-linear movements. To overcome these limitations, we propose ETTrack, a novel motion prediction method with an enhanced temporal motion predictor. Specifically, the motion predictor integrates a transformer model and a Temporal Convolutional Network (TCN) to capture both long-term and short-term motion patterns, and it predicts the future motion of individual objects based on the historical motion information. Additionally, we propose a novel Momentum Correction Loss function that provides additional information regarding the motion direction of objects during training. This allows the motion predictor to rapidly adapt to sudden motion variations and more accurately predict future motion. Our experimental results demonstrate that ETTrack achieves a competitive performance compared with state-of-the-art trackers on DanceTrack and SportsMOT, scoring 56.4 $$\%$$ % and 74.4 $$\%$$ % in HOTA metrics, respectively. Our work provides a robust solution for MOT in complex dynamic environments, which enhances the non-linear motion prediction capabilities of tracking algorithms.

ABSTRACT Ultrasound imaging is widely used in medical diagnosis, especially for fetal health assessment. However, the availability of high‐quality annotated ultrasound images is limited, which restricts the training of machine learning models. In this paper, we investigate the use of diffusion models to generate synthetic ultrasound images to improve the performance on fetal plane classification. We train different classifiers first on synthetic images and then fine‐tune them with real images. Extensive experimental results demonstrate that incorporating generated images into training pipelines leads to better classification accuracy than training with real images alone. The findings suggest that generating synthetic data using diffusion models can be a valuable tool in overcoming the challenges of data scarcity in ultrasound medical imaging.

The Sussex MK4 electrical impedance mammography system is especially designed for 3-D breast screening. It aims to diagnose breast cancer at an early stage when it is most treatable. Planar electrodes are employed in this system. The challenge with planar electrodes is the inaccuracy and poor sensitivity in the vertical direction for 3-D imaging. An enhanced image reconstruction algorithm using a duo-mesh method is proposed to improve the vertical accuracy and sensitivity. The novel part of the enhanced image reconstruction algorithm is the correction term. To evaluate the new algorithm, an image processing based error analysis method is presented, which not only can precisely assess the error of the reconstructed image but also locate the center and outline the center and outline the shape of the objects of interest. Although the enhanced image reconstruction algorithm and the image processing based error analysis method are designed for the Sussex MK4 system, they are applicable to all electrical impedance tomography systems, regardless of the hardware design. To validate the enhanced algorithm, performance results from simulations, phantoms and patients are presented.

A real‐time system for the detection and tracking of moving objects that becomes stationary in a restricted zone. A new pixel classification method based on the segmentation history image is used to identify stationary objects in the scene. These objects are then tracked using a novel adaptive edge orientation‐based tracking method. Experimental results have shown that the tracking technique gives more than a 95% detection success rate, even if objects are partially occluded. The tracking results, together with the historic edge maps, are analysed to remove objects that are no longer stationary or are falsely identified as foreground regions because of sudden changes in the illumination conditions. The technique has been tested on over 7 h of video recorded at different locations and time of day, both outdoors and indoors. The results obtained are compared with other available state‐of‐the‐art methods.

The application of an object-oriented (OO) methodology to the design of a platform for heterogeneous digital libraries with multiagent technologies is demonstrated. Emphasis is placed on maximizing the autonomy of the query processing activity. The Fusion OO paradigm is specifically employed as the basis for the design process due to the significance attributed to the development of object interfaces under static and dynamic conditions. Finally, characteristics of the proposed Domain Index Server (DIS) platform are contrasted with those of an alternative platform (the University of Michigan Digital Library, UMDL) by way of the respective Fusion descriptions. This identifies a different emphasis on the interface design between objects in the two platforms: the DIS system uses more dynamic links while the UMDL system focuses on permanent and constant links. Simulation of the two platforms provides performance data that demonstrates the higher capacity of the DIS scheme.

Optimal control requires the definition of a control policy from the behaviour of a plant, without the luxury of a desired reference trajectory. In the case of this work the direct method of optimal adaptive control is taken where feedback from the environment has no sign or directional information. Moreover, the case of continuous valued as opposed to binary valued control actions is required. The proposed architecture demonstrates extensive use of hierarchical partitioning of the problem in order to decompose the task into a composition of subtasks. The significance of variance terms in the design of RBFs is emphasized, and the entire network demonstrated on benchmark nonlinear control tasks. In each case the emphasis is towards the location of robust solutions without recourse to any a priori information.

A platform for performing multi-agent searches in heterogeneous digital libraries is proposed. This differs significantly from previous approaches by completely removing the concept of a centralized search engine. Specifically, the organization of information held on domain index servers is constrained to conform to a virtual tree representation based on facets and global keyword concept schema particular to the set of information providers associated with the domain of interest (e.g. preparatory intranet). Simulation studies are used to compare this platform against a digital library platform presently in use, which employs the traditional central server scheme. Improvements in terms of query service time and robustness are demonstrated.