TY  - MANSCPT
AU  - Syarifah Nabilah Syahirah Syed Abd Wahab,
TI  - Distance and speed predictive analytics based on dashcam video
SN  - THE0010030 (Local)
PY  - 2023///
CY  - Kuantan Pahang
PB  - UMPSA
KW  - Center for Mathematical Sciences
KW  - Dissertations
KW  - Universities and colleges
KW  - Final Year Report
N1  - Center for Mathematical Sciences; Bachelor of Applied Science In Data Analytics With Honour -- Universiti Malaysia Pahang – 2023; Includes bibliographical references
N2  - Dashcam is a device that capture the surroundings of a vehicle and is commonly utilised  as evidence in road crimes and accidents. Malaysian drivers who have difficulty  estimating the distance and speed between vehicles due to human error might be  prevented by utilising dashcam data, preventing an accident. The purpose of this research  is to develop a predictive modelling for estimating the distance and speed of vehicles on  the road. The goal is to reduce the incidence of accidents and crimes that occur on the  road. The study was conducted using deep learning techniques such as Long Short-Term  Memory (LSTM) and Convolutional Neural Network (CNN) methods. The dataset used  in this study was provided by Cybersecurity Malaysia and it consists of dashcam video  footage captured by Malaysian drivers. The dashcam footage was used to analysed the  real-life distance and speed between vehicles, which be used to make more accurate  predictions. Based on the research conducted using deep learning techniques such as  LSTM and CNN models on the provided dashcam video footage, the evaluation results  revealed moderate accuracy indicating room for improvement. The study provided  insights into vehicle speed distributions and distances, paving the way for future  advancements in distance and speed predictive analytics. The findings from this study  will help to enhance road safety in Malaysia by providing valuable insights and  improving drivers’ ability to estimate distance and speed, potentially reducing accidents  and crimes on the road
ER  -