Abstract
On-road vehicle detection and lane detection are integral parts of most advanced driver assistance systems (ADAS). In this paper, we introduce an integrated approach called Efficient Lane and Vehicle detection with Integrated Synergies (ELVIS), that exploits the inherent synergies between lane and on-road vehicle detection to improve the overall computational efficiency without compromising on the robustness of both the tasks. Detailed evaluations show that the vehicle detection component of ELVIS shows at least 50% lesser false alarms with equal or better detection rates, and reducing the computational costs by over 90% as compared to state-of-the-art vehicle detection methods. Similarly, the lane detection component shows more reliable lane feature extraction with average computation costs that are at least 35% lesser than existing techniques.
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