Muhammad Rafiqul Islam

Samia Sultana, Boshir Ahmed, Manoranjan Paul et al.

Lane marking detection is fundamental for both advanced driving assistance systems. However, detecting lane is highly challenging when the visibility of a road lane marking is low due to real-life challenging environment and adverse weather. Most of the lane detection methods suffer from four types of challenges: (i) light effects i.e., shadow, glare of light, reflection etc.; (ii) Obscured visibility of eroded, blurred, colored and cracked lane caused by natural disasters and adverse weather; (iii) lane marking occlusion by different objects from surroundings (wiper, vehicles etc.); and (iv) presence of confusing lane like lines inside the lane view e.g., guardrails, pavement marking, road divider etc. Here, we propose a robust lane detection and tracking method with three key technologies. First, we introduce a comprehensive intensity threshold range (CITR) to improve the performance of the canny operator in detecting low intensity lane edges. Second, we propose a two-step lane verification technique, the angle based geometric constraint (AGC) and length-based geometric constraint (LGC) followed by Hough Transform, to verify the characteristics of lane marking and to prevent incorrect lane detection. Finally, we propose a novel lane tracking technique, by defining a range of horizontal lane position (RHLP) along the x axis which will be updating with respect to the lane position of previous frame. It can keep track of the lane position when either left or right or both lane markings are partially and fully invisible. To evaluate the performance of the proposed method we used the DSDLDE [1] and SLD [2] dataset with 1080x1920 and 480x720 resolutions at 24 and 25 frames/sec respectively. Experimental results show that the average detection rate is 97.55%, and the average processing time is 22.33 msec/frame, which outperform the state of-the-art method.

Muhammad Rafiqul Islam, Manoranjan Paul

In computer vision applications, the visibility of the video content is crucial to perform analysis for better accuracy. The visibility can be affected by several atmospheric interferences in challenging weather-one of them is the appearance of rain streak. In recent time, rain streak removal achieves lots of interest to the researchers as it has some exciting applications such as autonomous car, intelligent traffic monitoring system, multimedia, etc. In this paper, we propose a novel and simple method by combining three novel extracted features focusing on temporal appearance, wide shape and relative location of the rain streak and we called it TAWL (Temporal Appearance, Width, and Location) method. The proposed TAWL method adaptively uses features from different resolutions and frame rates. Moreover, it progressively processes features from the up-coming frames so that it can remove rain in the real-time. The experiments have been conducted using video sequences with both real rains and synthetic rains to compare the performance of the proposed method against the relevant state-of-the-art methods. The experimental results demonstrate that the proposed method outperforms the state-of-the-art methods by removing more rain streaks while keeping other moving regions.