Object Tracking Based on Meanshift and Particle-Kalman Filter Algorithm with Multi Features

Iswanto, Irene Anindaputri; Choa, Tan William; Li, Bin

doi:10.1016/j.procs.2019.09.009

Cited by 17 publications

(8 citation statements)

References 9 publications

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“…Kalman filter performs better when the motion of the objects are linear. However, fishes have non linear motion which limits the effectiveness of Kalman filter [59]. To this end, in the present study, an algorithm is designed to incorporate swim direction information to reduce identity switches.…”

Section: B Object Tracking Methodsmentioning

confidence: 99%

Fish Tracking and Continual Behavioral Pattern Clustering Using Novel Sillago Sihama Vid (SSVid)

et al. 2023

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Aquaculture provides food security to many developing countries and enhances the socio-economic conditions of the fishermen. To enhance the productivity of the aquaculture, it is necessary to maintain stress free controlled eco-system for the fishes. For recognising the stress in fishes, behaviour analysis of fishes via tracking is imperative. Early detection of stress in fish facilitates fishermen to take precautionary measures promptly. Computer vision-based fish behaviour analysis of economically important fish species is challenging due to the lack of datasets, occlusions, rapid changes in swim directions etc. The present study proposes a multiple fish video dataset of an economically important species in a controlled environment, namely Sillago Sihama-Vid with accurate annotations. The study emulates the natural environment of Sillago Sihama in a large aquarium. This work proposes a novel fish tracking algorithm that incorporates swim direction information in addition to temporal, appearance, and spatial information. The inclusion of swim direction information reduces the number of identity switches. Comparative performance analysis of the proposed tracking algorithm with the conventional methods on the developed dataset highlights the performance efficiency. The proposed method has a clear performance improvement in MOTA, MOTP, IDSW and MT with respect to the other compared methods. The study also presents a novel unsupervised continual behaviour modelling strategy to model the evolving behaviours of the fishes. Further, interpretation of fish behaviour from the proposed behaviour modelling is performed to highlight the reliability of the proposed method. The significance of the proposed method is that, it is independent of training and labelled data. In addition, the method represents an innovative alternative to capture all the non observable behaviours of the fishes. The proposed tracking and behaviour modelling strategy act as a benchmark for developing algorithms to study fish behaviour via tracking. Finally, the dataset provides an opportunity for developing computer vision-based models to analyse the different behaviours of fish Sillago Sihama.INDEX TERMS Pattern analysis, fish behaviour analysis, fish tracking, fish trajectory dataset.

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Section: B Object Tracking Methodsmentioning

confidence: 99%

Fish Tracking and Continual Behavioral Pattern Clustering Using Novel Sillago Sihama Vid (SSVid)

et al. 2023

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“…Zhou et al [7] combined the Meanshift algorithm with Kalman filter algorithm to track the objects under occlusion and used Kalman filter algorithm to update the estimated position of the object under occlusion. Iswanto et al [8] proposed an object tracking algorithm combining Kalman filter with particle filter and the Meanshift, and used a color histogram and texture to improve the tracking accuracy. Zou et al [9] proposed an object tracking algorithm combining CamShift with Kalman filter, in which the search window range of the CamShift was narrowed through the prediction of the Kalman filter and then Kalman filter was corrected with the objective information obtained by CamShift.…”

Section: Related Workmentioning

confidence: 99%

Multi-Pedestrian Tracking in Crowded Scenes by Modeling Movement Behavior and Optimizing Kalman Filter

Jian-hong

Wang

2022

IEEE Access

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Most multi-object tracking methods have achieved good results in tracking multiple pedestrians with Kalman filter, but their tracking performance in crowded scenes is still poor due to pedestrian avoidance and frequent occlusion. In crowded scenes, the pedestrian trajectory prediction with Kalman filter alone is unreliable. In this paper, a two-dimensional field-of-view avoidance force model (AFM) is proposed to assist the Kalman filter prediction by sensing the avoidance force and then complete pedestrian tracking. In the model, each pedestrian has a two-dimensional field of view to perceive the avoidance force, which determines the next predicted trajectory. In real scenes, pedestrians tend to be more concerned about the surrounding area, so different areas are set to simulate the attention mechanisms of pedestrians in real scenes. In the FairMOT model, AFM is used to optimize the pedestrian state values of Kalman filter prediction and the optimized model is trained on the MOT16 public dataset. The experimental results on the MOT20 benchmark dataset show that compared with the mainstream tracking model FairMOT, our method respectively improves MOTA by 2.7% and IDF1 by 2.2%. Our method also achieves the good performance on MOT15, MOT16, and MOT17 tracking benchmarks.

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“…Based on the crack data set, the improved K-means segmentation algorithm is used to identify crack pictures on the surface of the concrete with moss, fallen leaves, or water stains, and the identification results are combined with the K-means algorithm [24], means shift algorithm [25], and fuzzy C-means algorithm for comparative analysis, as shown in Figure 13.…”

Section: Comparison With Clustering Algorithmsmentioning

confidence: 99%

Semantic Recognition and Location of Cracks by Fusing Cracks Segmentation and Deep Learning

Chen

et al. 2021

Complexity

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For a long time, cracks can appear on the surface of concrete, resulting in a number of safety problems. Traditional manual detection methods not only cost money and time but also cannot guarantee high accuracy. Therefore, a recognition method based on the combination of convolutional neural network and cluster segmentation is proposed. The proposed method realizes the accurate identification of concrete surface crack image under complex background and improves the efficiency of concrete surface crack identification. The research results show that the proposed method not only classifies crack and noncrack efficiently but also identifies cracks in complex backgrounds. The proposed method has high accuracy in crack recognition, which is at least 97.3% and even up to 98.6%.

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Object Tracking Based on Meanshift and Particle-Kalman Filter Algorithm with Multi Features

Cited by 17 publications

References 9 publications

Fish Tracking and Continual Behavioral Pattern Clustering Using Novel Sillago Sihama Vid (SSVid)

Fish Tracking and Continual Behavioral Pattern Clustering Using Novel Sillago Sihama Vid (SSVid)

Multi-Pedestrian Tracking in Crowded Scenes by Modeling Movement Behavior and Optimizing Kalman Filter

Semantic Recognition and Location of Cracks by Fusing Cracks Segmentation and Deep Learning

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