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Browsing by Author "Kollazhi Manghat, Surya"
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Item Forward Collision Prediction with Online Visual Tracking(IEEE, 2019-09) Kollazhi Manghat, Surya; El-Sharkawy, Mohamed; Electrical and Computer Engineering, School of Engineering and TechnologySafety is the key aspect when comes to driving. Self-driving vehicles are equipped with driver-assistive technologies like Adaptive Cruise Control, Forward Collision Warning system (FCW) and Collsion Mitigation by Breaking (CMbB) to ensure safety while driving. This paper proposes a method by following a lean way of multi-target tracking implementation and 3D bounding box detection without processing much visual information. Object Tracking is an integral part of environment sensing, which enables the vehicle to estimate the surrounding object’s trajectories to accomplish motion planning. The advancement in the object detection methods greatly benefits when following the tracking by detection approach. This will lead to less complex tracking methodology and thus decreasing the computational cost. Estimation based on particle filter is added to precisely associate the tracklets with detections. The model estimates and plots bounding box for the objects in its camera range and predict the 3D positions in camera coordinates from monocular camera data using a deep learning combined with geometric constraints using 2D bounding box, then the actual distance from the vehicle camera is calculated. The model is evaluated on the KITTI car dataset.Item Multi Sensor Multi Object Tracking in Autonomous Vehicles(2019-12) Kollazhi Manghat, Surya; El-Sharkawy, Mohamed; Rizkalla, Maher; King, BrianSelf driving cars becoming more popular nowadays, which transport with it's own intelligence and take appropriate actions at adequate time. Safety is the key factor in driving environment. A simple fail of action can cause many fatalities. Computer Vision has major part in achieving this, it help the autonomous vehicle to perceive the surroundings. Detection is a very popular technique in helping to capture the surrounding for an autonomous car. At the same time tracking also has important role in this by providing dynamic of detected objects. Autonomous cars combine a variety of sensors such as RADAR, LiDAR, sonar, GPS, odometry and inertial measurement units to perceive their surroundings. Driver-assistive technologies like Adaptive Cruise Control, Forward Collision Warning system (FCW) and Collision Mitigation by Breaking (CMbB) ensure safety while driving. Perceiving the information from environment include setting up sensors on the car. These sensors will collect the data it sees and this will be further processed for taking actions. The sensor system can be a single sensor or multiple sensor. Different sensors have different strengths and weaknesses which makes the combination of them important for technologies like Autonomous Driving. Each sensor will have a limit of accuracy on it's readings, so multi sensor system can help to overcome this defects. This thesis is an attempt to develop a multi sensor multi object tracking method to perceive the surrounding of the ego vehicle. When the Object detection gives information about the presence of objects in a frame, Object Tracking goes beyond simple observation to more useful action of monitoring objects. The experimental results conducted on KITTI dataset indicate that our proposed state estimation system for Multi Object Tracking works well in various challenging environments.Item A Multi Sensor Real-time Tracking with LiDAR and Camera(IEEE, 2020-01) Kollazhi Manghat, Surya; El-Sharkawy, Mohamed; Electrical and Computer Engineering, School of Engineering and TechnologySelf driving cars are equipped with various driver-assistive technologies (ADAS) like Forward Collision Warning system (FCW), Adaptive Cruise Control and Collision Mitigation by Breaking (CMbB) to ensure safety. Tracking plays an important role in ADAS systems for understanding dynamic environment. This paper proposes 3D multi-target tracking method by following a lean way of implementation using object detection with aim of real time. Object Tracking is an integral part of environment sensing, which enables the vehicle to estimate the surrounding object's trajectories to accomplish motion planning. The advancement in the object detection methodologies benefits greatly when following the tracking by detection approach. The proposed method implemented 2D tracking on camera data and 3D tracking on LiDAR point cloud data. The estimated state from each sensors are fused together to come with a more optimal state of objects present in the surrounding. The multi object tracking performance has evaluated on publicly available KITTI dataset.