MOTION AND APPERANCE BASED DEETECTION BASED ON DEEP LEARNING FOR AUTONOMOUS DRIVING

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chaturi shahkumar
G.RANI
NAZREEN SULTANA
G.NEERAJ

Abstract

For autonomous driving, moving objects like vehicles and pedestrians are of critical importance as they primarily influence the maneuvering and braking of the car. Typically, they are detected by motion segmentation of tensor optical flow augmented by a SSD based object detector for capturing semantics. In this project, our aim is to jointly model motion and appearance cues in a single short detector. We propose a novel two-stream architecture for joint learning of object detection and motion segmentation. We designed three different flavors of our network to establish systematic comparison. It is shown that the joint training of tasks significantly improves accuracy compared to training them independently. Although motion segmentation has relatively fewer data than vehicle detection. The shared fusion encoder benefits from the joint training to learn a generalized representation. We created our own publicly available dataset that contains some frame sets that is passed through the tensor flow object to algorithm model. As compare with the pervious algorithm the confidence level and prediction level increases the map score  up to 20.2%. We also evaluated our algorithm on the non-automotive DAVIS or motion  dataset and obtained accuracy close to the state-of-the-art performance.

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How to Cite
shahkumar, chaturi, G.RANI, NAZREEN SULTANA, and G.NEERAJ. “MOTION AND APPERANCE BASED DEETECTION BASED ON DEEP LEARNING FOR AUTONOMOUS DRIVING”. Technix International Journal for Engineering Research, vol. 9, no. 6, June 2022, pp. 122-7, https://tijer.org/index.php/tijer/article/view/208.
Section
Research Articles

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