Search Results (4)

The fragmentation of vessel tracks represents a significant challenge in the context of high-frequency surface wave radar (HFSWR) tracking. This paper proposes a new track segment association (TSA) algorithm that integrates optimal tracklet assignment, iterative discrimination, and multi-stage association. This paper reformulates the optimal tracklet assignment task as an optimal state search problem for modeling and solution purposes. To determine whether competing old and new tracklets can be associated, we assume the existence of a public state that represents the correlation between the tracklets. However, due to track fragmentation, this public state remains unknown. We need to search for the optimal public state of all candidate tracklet pairs within a feasible parameter space, using a fitness function value as the evaluation criterion. The old and new tracklets pairs that match the optimal public state are considered optimally associated. Since the solution process involves searching for the optimal state across multiple dimensions, it constitutes a high-dimensional optimization problem. To accomplish this task, the catch fish optimization algorithm (CFOA) is employed for its ability to escape local optima and handle high-dimensional optimization, enhancing the reliability of tracklet assignment. Furthermore, we achieve precise one-to-one associations by assigning new tracklet to old tracklet through the optimal tracklet assignment method we proposed, a process we abbreviate as AN2O, and its inverse process, which assigns old tracklet to new tracklet, abbreviated as AO2N. This dual approach is further complemented by an iterative discrimination mechanism that evaluates unselected tracklets to identify potential associations that may exist. The algorithm effectiveness of the proposed is validated using field experiment data from HFSWR in the Bohai Sea region, demonstrating its capability to accurately process complex tracklet data. Full article

A compact high-frequency surface wave radar, used for target detection, suffers from a low signal-to-noise ratio, low detection probability, a high false alarm rate, and low positioning accuracy; this is due to its low transmit power and the reduced aperture size of the receiving antenna array. When target tracking algorithms are applied to compact high-frequency surface wave radar data, track fragmentation often occurs and a long track may be broken into several track segments (a.k.a. tracklets), which degrade the tracking continuity for a maritime surveillance system. We present a multi-stage vessel tracklet association method, based on bidirectional prediction and optimal assignment, to associate the broken tracklets belonging to the same target, and connect them to form one continuous track in a multi-target tracking scenario. Firstly, two global motion parameters, i.e., the average heading and average speed, were, respectively, extracted from the newly initiated and terminated tracklets as features for a rough tracklet association, then k-means clustering was used to produce the preliminary tracklet pairs. Subsequently, the temporal and spatial constraints on the initiated and terminated tracklets were considered to refine the preliminary tracklet pairs, to obtain the candidate tracklet pairs. Finally, the tracklet association costs were calculated using Doppler velocity, range, and azimuth to determine the similarity between tracklets in the candidate tracklet pairs, and an association cost matrix was obtained. Then an optimal assignment method based on Jonker–Volgenant–Castanon algorithm was applied to the association matrix to achieve optimal tracklet matching by minimizing the total association costs. Tracklet association experiments with both simulated and field data were conducted; experimental results show that, compared with existing track segment association methods, the association accuracy of the proposed method is significantly improved with better tracking continuity. Full article

Video scene graph generation (ViDSGG), the creation of video scene graphs that helps in deeper and better visual scene understanding, is a challenging task. Segment-based and sliding-window based methods have been proposed to perform this task. However, they all have certain limitations. This study proposes a novel deep neural network model called VSGG-Net for video scene graph generation. The model uses a sliding window scheme to detect object tracklets of various lengths throughout the entire video. In particular, the proposed model presents a new tracklet pair proposal method that evaluates the relatedness of object tracklet pairs using a pretrained neural network and statistical information. To effectively utilize the spatio-temporal context, low-level visual context reasoning is performed using a spatio-temporal context graph and a graph neural network as well as high-level semantic context reasoning. To improve the detection performance for sparse relationships, the proposed model applies a class weighting technique that adjusts the weight of sparse relationships to a higher level. This study demonstrates the positive effect and high performance of the proposed model through experiments using the benchmark dataset VidOR and VidVRD. Full article

Pedestrian tracking in dense crowds is a challenging task, even when using a multi-camera system. In this paper, a new Markov random field (MRF) model is proposed for the association of tracklet couplings. Equipped with a new potential function improvement method, this model can associate the small tracklet coupling segments caused by dense pedestrian crowds. The tracklet couplings in this paper are obtained through a data fusion method based on image mutual information. This method calculates the spatial relationships of tracklet pairs by integrating position and motion information, and adopts the human key point detection method for correction of the position data of incomplete and deviated detections in dense crowds. The MRF potential function improvement method for dense pedestrian scenes includes assimilation and extension processing, as well as a message selective belief propagation algorithm. The former enhances the information of the fragmented tracklets by means of a soft link with longer tracklets and expands through sharing to improve the potentials of the adjacent nodes, whereas the latter uses a message selection rule to prevent unreliable messages of fragmented tracklet couplings from being spread throughout the MRF network. With the help of the iterative belief propagation algorithm, the potentials of the model are improved to achieve valid association of the tracklet coupling fragments, such that dense pedestrians can be tracked more robustly. Modular experiments and system-level experiments are conducted using the PETS2009 experimental data set, where the experimental results reveal that the proposed method has superior tracking performance. Full article