Neural computing-based pedestrian detection from image sequence

Preventing a traffic accident is a good way to solve many problems in the world for the current generation surrounding with many automotive technologies causing many people's death from the accident. The prevention makes an important impact to every society for making many people more safety and improving their lives' quality. In the fact, the primary cause is mostly drivers' carelessness and lacking of control, which might be because of addiction of drugs, resulting that a pedestrian walking on a road pains and then becomes dead. Therefore the problem can be solved using a computer for analysis and making a decision as a human called pedestrian detection. This study uses many several enhancement algorithms and processes for detecting integrated with an analysis based on a neural computing to decide whether a detected object is a pedestrian. Moreover the study shows an experimental result that the procedure is sufficient and efficient to be a fundamental knowledge for the next related work including a real-life traffic situation.