DEEP-SEE: joint object detection, tracking and recognition with application to visually impaired navigational assistance

Abstract : In this paper, we introduce the so-called DEEP-SEE framework that jointly exploits computer vision algorithms and deep convolutional neural networks (CNNs) to detect, track and recognize in real time objects encountered during navigation in the outdoor environment. A first feature concerns an object detection technique designed to localize both static and dynamic objects without any a priori knowledge about their position, type or shape. The methodological core of the proposed approach relies on a novel object tracking method based on two convolutional neural networks trained offline. The key principle consists of alternating between tracking using motion information and predicting the object location in time based on visual similarity. The validation of the tracking technique is performed on standard benchmark VOT datasets, and shows that the proposed approach returns state-of-the-art results while minimizing the computational complexity. Then, the DEEP-SEE framework is integrated into a novel assistive device, designed to improve cognition of VI people and to increase their safety when navigating in crowded urban scenes. The validation of our assistive device is performed on a video dataset with 30 elements acquired with the help of VI users. The proposed system shows high accuracy (>90%) and robustness (>90%) scores regardless on the scene dynamics
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Sensors, MDPI, 2017, 17 (11), pp.2473-1 - 2473-24. 〈10.3390/s17112473〉
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https://hal.archives-ouvertes.fr/hal-01687406
Contributeur : Médiathèque Télécom Sudparis & Institut Mines-Télécom Business School <>
Soumis le : jeudi 18 janvier 2018 - 14:36:17
Dernière modification le : jeudi 31 mai 2018 - 09:12:02

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Ruxandra Tapu, Bogdan Mocanu, Titus Zaharia. DEEP-SEE: joint object detection, tracking and recognition with application to visually impaired navigational assistance. Sensors, MDPI, 2017, 17 (11), pp.2473-1 - 2473-24. 〈10.3390/s17112473〉. 〈hal-01687406〉

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