Pedestrian detection using haarcascade
Computer Vision Plays a vital role in traffic management and surveillance systems and has been an active research area in the past years. In systems like these, the detection of pedestrian and also classification of the vehicle plays a major role. The datasets are traffic videos of urban environment taken from various cities around the world which were used to train the classifier hence generating a robust classifier. The proposed approach is computationally less expensive with faster processing speed.
Haar Cascade Classifiers : I implement this using the Haar Cascade classifier. Haar Cascade classifier is an effective object detection approach.
So, let’s try to understand what these Haar Cascade Classifiers are.This is basically a machine learning based approach where a cascade function is trained from a lot of images both positive and negative. Based on the training it is then used to detect the objects in the other images.
So, how this works is they are huge individual .xml files with a lot of feature sets and each xml corresponds to a very specific type of use case.
ret, frame = cap.read() #frame = cv2.resize(frame, None,fx=0.5, fy=0.5, interpolation = cv2.INTER_LINEAR) gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Pass frame to our body classifier bodies = body_classifier.detectMultiScale(gray, 1.2, 3) # Extract bounding boxes for any bodies identified for (x,y,w,h) in bodies: cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 255), 2) cv2.imshow('Pedestrians', frame) if cv2.waitKey(1) == 13: #13 is the Enter Key break
Pedestrian detection using Yolo
Pedestrian detection in image or video data is a very important and challenging task in security surveillance. The difficulty of this task is to locate and detect pedestrians of different scales in complex scenes accurately.
To solve these problems, a deep neural network (RT-YOLOv3) is proposed to realize real-time pedestrian detection at different scales in security monitoring. RT-YOLOv3 improves the traditional YOLOv3 algorithm. Firstly, the deep residual network is added to extract vehicle features.
Then six convolutional neural networks with different scales are designed and fused with the corresponding scale feature maps in the residual network to form the final feature pyramid to perform pedestrian detection tasks. This method can better characterize pedestrians.
In order to further improve the accuracy and generalization ability of the model, a hybrid pedestrian data set training method is used to extract pedestrian data.