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- W2806748897 abstract "Object tracking is essential step of many computer vision applications. It deals with locating objects across the frames in videos. There are several useful applications in day-to-day life such as: human-computer interaction, security and surveillance, video, augmented reality, traffic management, aviation control medical imaging etc. There are various challenges when it comes to dealing with object tracking, such as image illumination, motion object, occlusion, scaling etc. In this paper a study of a hybrid model for object tracking is performed. The hybrid model is based two existing algorithms: saliency prior context based model and patching based model. The algorithm generates the similarity based map for the image, then tracks the object in form of patches, whose combined effect is used as a vote map. Then using image signature and spatio-temporal computation a confidence map is generated to predict the next position. Numerous experiment results show that hybrid algorithm achieves significant improvement our saliency prior context based tracker." @default.
- W2806748897 created "2018-06-13" @default.
- W2806748897 creator A5085070970 @default.
- W2806748897 date "2017-11-01" @default.
- W2806748897 modified "2023-09-28" @default.
- W2806748897 title "Object tracking based hybrid context and image patching models" @default.
- W2806748897 doi "https://doi.org/10.1109/icici.2017.8365392" @default.
- W2806748897 hasPublicationYear "2017" @default.
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