From a theoretical perspective, we establish a mathematical framework to integrate SLAM and moving object tracking, which provides a solid basis for understanding and solving the whole problem. Such an approach is similar to existing SLAM algorithms, but with additional structure to allow for motion modelling of the generic objects.
Unfortunately, it is computationally demanding and infeasible.
By maintaining separate posteriors for the stationary objects and the moving objects, the resulting estimation problems are much lower dimensional than SLAM with GO. From a practical perspective, we develop algorithms for dealing with the implementation issues on perception modelling, motion modelling and data association.
Regarding perception modelling, a hierarchical object based representation is presented to integrate existing feature-based, grid-based and direct methods.
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The sampling- and correlation-based range image matching algorithm is developed to tackle the problems arising from uncertain, sparse and featureless measurements. With regard to motion modelling, we describe a move-stop hypothesis tracking algorithm to tackle the difficulties of tracking ground moving objects.
Algorithm Research on Moving Object Detection of Surveillance Video Sequence
Kinematic information from motion modelling as well as geometric information from perception modelling is used to aid data association at different levels. By following the theoretical guidelines and implementing the described algorithms, we are able to demonstrate the feasibility of SLAMMOT using data collected from the Navlab8 and Navlab11 vehicles at high speeds in crowded urban environments.
Google Scholar. Dai, D. A spatial-temporal approach for segmentation of moving and static objects in sector scan sonar image sequences, Proceedings of the 5th International Conference on Image Processing and its Applications , — Ding, K.
Object tracking master thesis GitHub - roemil/Multi-Object-Tracking: Master Thesis
Gonzalez, R. E, Digital Image Processing , 2nd ed. Kalyan, B.
Sonar and vision based navigation schemes for autonomous underwater vehicles, Proceedings of the 8th International Conference on Control , Automation , Robotics and Vision , — Kotecha, J. Lu, Y. Ma, Y. Qi, N.
Robust Single Object Tracking via Fully Convolutional Siamese Networks
Perry, S. A recurrent neural network for detecting objects in sequences of sector-scan sonar images, IEEE Journal of Oceanic Engineering , 29 3 : — Petillot, Y.
Underwater vehicle obstacle avoidance and path planning using a multi-beam forward sonar, IEEE Journal of Oceanic Engineering , 26 2 : — Ruiz, T. A comparison of inter-frame feature measures for robust object classification in sector scan sonar image sequences, IEEE Journal of Oceanic Engineering , 24 4 : — Wang, S. A fast underwater optical image segmentation algorithm based on a histogram weighted fuzzy C-means improved by PSO, Journal of Marine Science and Application , 10 1 : 70— Williams, N.
Classification of sector-scanning sonar image sequences, Proceedings of the 5th International Conference on Image Processing and Its Applications , — Xu, Y.