A NOVEL HYBRID DEEP LEARNING APPROACH FOR 3D OBJECT DETECTION AND TRACKING IN AUTONOMOUS DRIVING

Abstract

Recently Object detection and tracking using fusion of LiDAR and RGB camera for the autonomous vehicle environment is a challenging task. The existing
works initiates several object detection and tracking frameworks using Artificial
Intelligence (AI) algorithms. However, they were limited with high false positives and computation time issues thus lacking the performance of autonomous
driving environment. The existing issues are resolved by proposing Hybrid
Deep Learning based Multi Object Detection and Tracking (HDL-MODT) using sensor fusion methods. The proposed work performs fusion of solid state
LiDAR, Pseudo LiDAR, and RGB camera for improving detection and tracking
quality. At first, the multi-stage preprocessing is done in which noise removal is
performed using Adaptive Fuzzy Filter (A-Fuzzy). The pre-processed fused image is then provided for instance segmentation to reduce the classification and
tracking complexity. For that, the proposed work adopts Lightweight General
Adversarial Networks (LGAN). The segmented image is provided for object
detection and tracking using HDL. For reducing the complexity, the proposed
work utilized VGG-16 for feature extraction which forms the feature vectors.
The features vectors are then provided for object detection using YOLOv4.
Finally, the detected objects were tracked using Improved Unscented Kalman
Filter (IUKF) and mapping the vehicles using time based mapping by considering their RFID, velocity, location, dimension and unique ID. The simulation of
the proposed work is carried out using MATLAB R2020a simulation tool and
performance of the proposed work is compared with several metrics that show
that the proposed work outperforms than the existing works.