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This paper explains the process to train and infer the pedestrian detection problem using the TensorFlow* deep learning framework on Intel® architecture. A transfer learning approach was used by taking the frozen weights from a Single Shot MultiBox Detector model with Inception* v2 topology trained on the Microsoft Common Objects in Context* (COCO) dataset, and then using those weights on a Caltech pedestrian dataset to train and validate. The trained model was used for inference on traffic videos to detect pedestrians. The experiments were run on Intel® Xeon® Gold processor-powered systems. Improved model detection performance was observed by creating a new dataset from the Caltech images, and then selectively filtering based on the ratio of image size to object size and training the model on this new dataset.
With the world becoming more vulnerable to pronounced security threats, intelligent video surveillance systems are becoming increasingly significant. Video monitoring in public areas is now common; prominent examples of its use include the provision of security in urban centers and the monitoring of transportation systems. These systems can monitor and detect many elements, such as pedestrians, in a given interval of time. Detecting a pedestrian is an essential and …
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