Multi-object tracking (MOT) is a technique that aims to detect multiple objects in a single frame and maintain their identities in a given video sequence. Currently, the tracking-by-detection paradigm stands out as the most effective approach for MOT, encompassing object detection, person re-identification (Re-ID), and data association. In this paper, we propose enhancements to the Re-ID and data association modules, emphasizing the pivotal role of appearance information in achieving robust tracking quality. To capture more discriminative features, we introduce a network based on partial-level features. We also employ reparameterization to remove residual connection blocks from the backbone network without altering the original output to enhance tracking accuracy while optimizing computational efficiency. Moreover, the utilization of bounding boxes significantly influences tracking performance. We introduce an adaptive threshold mechanism and incorporate gating functions for data association, ensuring efficient use of each bounding box while minimizing trajectory fragmentation and missed detections. Experimental results illustrate that our proposed method attains high detection and tracking accuracy, particularly excelling in challenging video sequences. Our approach demonstrates comparable performance to state-of-the-art trackers on various benchmarks, including MOT17 and MOT20, where MOT20 presents frequent and severe interactions and occlusions.

The Internet of Things (IoT) has revolutionized industrial environments by offering various solutions for control and automation. However, the increasing number of machines and the resulting massive traffic volumes pose significant challenges in achieving Quality of Service (QoS) and avoiding network overload. In addition, it is important to note that various categories of applications within the IoT necessitate distinct QoS considerations. Moreover, traffic allocation among IoT servers should be based on their respective capacity levels. In order to tackle these challenges, this study presents an innovative framework utilizing Software-Defined Networking (SDN) to effectively meet the QoS demands of diverse IoT services while also achieving traffic equilibrium among IoT servers. The experimental results indicate improved IoT QoS parameters, including throughput and delay, while maintaining a low control plane overhead.

In the standard bag‐of‐visual‐words model, the relationship between visual words and geometric structure information embedding in Voronoi cells is important for expressing the topology of the feature space. However, this information is usually ignored by recent works. To overcome it, the authors proposed a hybrid heterogeneous structure model (HHSM), where local hyperspheres and local structure subspaces are applied to simulate the intrinsic structure of the feature space. Firstly, the local hypersphere is formed by choosing some links between parts of visual words, with the use of a proposed decision strategy derived from k ‐dense neighbour algorithm. In order to capture the geometric structure information around the visual word, they then construct the local structure subspace with the transformed PCA principal vectors of the visual features within a Voronoi cell. Finally, this study introduces a novel feature encoding method based on the HHSM. Experiments are conducted on 15‐Scenes, Pascal VOC2007, Caltech101, Caltech256 and MIT Indoor 67 datasets, which include 4485, 9963, 9146, 30607 and 15620 images, respectively. The results demonstrate the effectiveness of the proposed method in improving the accuracy of the classification. In addition, the proposed method achieves comparable performance when combined with CNN local features.

In natural images, it remains challenging to estimate dissimilarities between image elements for scene representation due to gradual variations of illuminations, textures or clutters. To tackle this problem, we utilise a path‐based bottleneck analysis method that captures the semantic information between image elements to measure the dissimilarity. By integrating both the spatial continuity and feature consistency into the understanding of the semantic information, we detect the bottlenecks on the proposed double‐S path to define the bottleneck distance, which demonstrates a favourable capability of grouping image elements that follow a similar pattern and separating different ones. In the experiments, the method is proved to be robust to noises and invariant to changing illumination and arbitrary scales in natural images. Tests on some challenging datasets validate the advantage of applying the path‐based bottleneck distance in image ranking and salient object detection.

The authors propose a tracking algorithm based on the reliability analysis of the convolutional neural network to avoid drift. In general, most tracking algorithms implemented with the deep network consist of a single network; they obtain the tracking results according to the confidence and perform updates with the samples, which are collected based on the previous target state. However, this kind of algorithm relies heavily on the accuracy of tracking results, and slight deviations can lead to improperly labelled training samples and degrade the network. Therefore, they design a verification network to guarantee the reliability of the tracking network by correcting the results and it can be connected to a tracking network by sharing convolutional layers. The reliability verification network estimates the accuracy of the results of the tracking network and discards ambiguous results to avoid accumulating errors. Specifically, the verification network can distinguish the target from the confused candidates more precisely because of the optimised training data. The training samples of the verification network consist of characteristics and labels, and they are optimised by feature selection and label enhancement, respectively. The experimental results illustrate the outstanding performance compared with several state‐of‐the‐art methods on the challenging video sequences.

Local saliency of a target describes the difference between the target and its surroundings, an effective way to improve the efficiency of searching for a target is to enhance its local saliency. To resolve the visual search and localization problem in vision-based robotics, this paper proposes a stepwise method that approaches the target in a successive way. The method has two main features: (1) a search path is established in searching for objects with high saliency based on saliency analysis; (2) In the process of target search and localization, the relationship between object locations is utilized to reduce the size of search regions step by step and, simultaneously, enhance the saliency of objects inside regions. Experimental results indicate the efficiency of the method for target search and localization under complex scenes.

Eye movements provide important insight into the cognitive processes underlying the visual search tasks. For image understanding, although the visual search patterns of different observers while studying the same scene bear some common characteristics, the idiosyncrasy associated with individual observers provides both research opportunities and challenges. The aim of this paper is to study the spatial characteristics of visual search, together with the intrinsic visual features of the fixation points for comparing different visual search strategies. An analysis framework based on earth mover's distance (EMD) in normalized anatomical space is proposed, and the results are demonstrated with high resolution computed tomography (HRCT) images of the lungs. The study shows that through the effective use of both spatial and feature space representation, it is possible to untangle what appear to be uncorrelated fixation distribution patterns to reveal common visual search behaviors.

This paper presents a new technique for extracting visual saliency from experimental eye tracking data. An eye-tracking system is employed to determine which features that a group of human observers considered to be salient when viewing a set of video images. With this information, a biologically inspired saliency map is derived by transforming each observed video image into a feature space representation. By using a feature normalisation process based on the relative abundance of visual features within the background image and those dwelled on eye tracking scan paths, features related to visual attention are determined. These features are then back projected to the image domain to determine spatial areas of interest for unseen video images. The strengths and weaknesses of the method are demonstrated with feature correspondence for 2D to 3D image registration of endoscopy videos with computed tomography data. The biologically derived saliency map is employed to provide an image similarity measure that forms the heart of the 2D/3D registration method. It is shown that by only processing selective regions of interest as determined by the saliency map, rendering overhead can be greatly reduced. Significant improvements in pose estimation efficiency can be achieved without apparent reduction in registration accuracy when compared to that of using a non-saliency based similarity measure.

This paper presents a new framework for capturing intrinsic visual search behavior of different observers in image understanding by analysing saccadic eye movements in feature space. The method is based on the information theory for identifying salient image features based on which visual search is performed. We demonstrate how to obtain feature space fixation density functions that are normalized to the image content along the scan paths. This allows a reliable identification of salient image features that can be mapped back to spatial space for highlighting regions of interest and attention selection. A two-color conjunction search experiment has been implemented to illustrate the theoretical framework of the proposed method including feature selection, hot spot detection, and back-projection. The practical value of the method is demonstrated with computed tomography image of centrilobular emphysema, and we discuss how the proposed framework can be used as a basis for decision support in medical image understanding.

This paper presents a new method of knowledge gathering for decision support in image understanding based on information extracted from the dynamics of saccadic eye movements. The framework involves the construction of a generic image feature extraction library, from which the feature extractors that are most relevant to the visual assessment by domain experts are determined automatically through factor analysis. The dynamics of the visual search are analyzed by using the Markov model for providing training information to novices on how and where to look for image features. The validity of the framework has been evaluated in a clinical scenario whereby the pulmonary vascular distribution on Computed Tomography images was assessed by experienced radiologists as a potential indicator of heart failure. The performance of the system has been demonstrated by training four novices to follow the visual assessment behavior of two experienced observers. In all cases, the accuracy of the students improved from near random decision making (33%) to accuracies ranging from 50% to 68%.

The identification of bronchi on Computed Tomography (CT) images of the lungs provides valuable clinical information in patients with suspected airways diseases including bronchiectasis, emphysema, or constrictive obliterative bronchiolitis. The automated recognition of the airways is, therefore, an important part of a diagnosis aid system for resolving potential ambiguities associated with intensity-based feature extractors. On CT images, near-perpendicular cross sections of bronchi normally appear as elliptical rings and this paper presents a novel technique for their recognition. The proposed method, the edge-radius-symmetry (ERS) transform, is based on the analysis of the distribution of edges in local polar coordinates. Pixels are ranked according to local edge (E) strength, radial (R), uniformity and local symmetry (S). A discrete implementation of the technique is provided which reduces the computational cost of the ERS transform by using a geometric approximation of the intensity patterns. The identification of the adjacent pulmonary vessels with template matching then allows for the automated measurement of bronchial dilatation and bronchial wall thickening. Computationally, the method compares favorably with other methods such as the Hough transform. Noise-sensitivity of the technique was evaluated on a set of synthetic images and nine patients under investigation for suspected airways disease. Agreement for the automated scoring of the presence and severity of bronchial abnormalities was demonstrated to be comparable to that of an experienced radiologist (kappa statistics kappa > 0.5 ).