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Fast pedestrian detection using smart roi separation and integral image based feature extraction.
| Content Provider | CiteSeerX |
|---|---|
| Author | Bineesh T., R. Simon, Philomina |
| Abstract | Abstract – This paper discusses a fast pedestrian detection system for near infrared imaging system. The Advanced Driver Assistance Systems include pedestrian detection system to avoid accidents. Most pedestrian detection systems produce false alarms or they are not fast. To overcome these issues a new approach for pedestrian detection is presented here. Initially the foreground is segmented by a smart region detection method to generate candidates. Then a series of rejecters are integrated to filter out non-pedestrians. After filtering out typical non-pedestrian objects, the remaining number of region of interest (ROI) is verified using a Support Vector Machine (SVM) classifier with Histogram of Oriented Gradients (HOG) feature. A second level classification is performed with HAAR feature to reduce the False alarms. The integral image representation is used for extracting both features, which significantly improves the computation speed. Experimental result shows that the proposed pedestrian detection system is suitable in the real-time environment, as it gives high detection rate and very low false alarm rate. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Fast Pedestrian Detection Feature Extraction Pedestrian Detection System Smart Roi Separation Integral Image False Alarm Computation Speed Pedestrian Detection Typical Non-pedestrian Object Haar Feature New Approach Fast Pedestrian Detection System Advanced Driver Assistance System Real-time Environment Second Level Classification High Detection Rate Experimental Result Show Support Vector Machine Smart Region Detection Method Integral Image Representation Low False Alarm Rate Imaging System Oriented Gradient |
| Content Type | Text |
| Resource Type | Article |
