# Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey
## 5 Deep Learning for Pedestrian and Cyclist Detection  
A subset of artificial intelligence and machine learning, deep learning (DL) was first introduced in the 1990s but has only recently been able to be used due to advancements and decline in costs of computational equipment (e.g., graphics processing units (GPUs)) and efficient training algorithms [44; 62]. In particular, the Convolutional Neural Networks (CNN) algorithms have been used in the field of computer vision and image analysis [59] for object detection [51], image classification [7] and face recognition [63]. CNN approaches have been considered state-of-the-art in this field of computer vision. Convolutional Neural Networks (CNNs) are a type of DL technique that has high performance in many fields as object recognition and classification. These objects can include faces and handwritten numerals and letters. The robustness of CNNs stems from the fact that they are able to extract information from raw-pixel content and learn features automatically [44]. It does this by performing various operations, typically some combination of filtering, pooling and non-linear activation. One benefit of using CNNs for feature extraction, when compared to hand-crafted methods, is that CNNs learn features from the images without explicit programming. Since 2012, new approaches based on DL techniques have developed for pedestrian detection such as AlexNet [1], a CNN technique developed by [PERSON] and named after the developer [64]. AlexNet was trained used in an ImageNet dataset. For ImageNet, the custom is to report two error rates; top-1 (full testset) and top-5 (fraction of testset). AlexNet error rates were 37.5% and 17.0% for top-1 and top-5 respectively. Prior to AlexNet's results, the best performance in terms of error rates were 47.1% and 28.2%. These results aided in the designing of hardware to improve the performance of CNNs for an increased accuracy in detection as well as the affordability of training of the CNNs. DL uses multiple layers, which are able to extract features, such as edges or patterns in images and use these features to classify an object. In this way, deep neural networks such as CNNs are used for feature learning to recognise objects such as pedestrians [59; 65; 66; 67]. Feed-forward neural networks comprise of a series of computational nodes known as neurons that are interconnected for information processing. This is also known as multi-layer perceptron (MLP). The nodes form layers that are interconnected through parameter values called weights. The neuron functions as a logistic regression classifier. The neurons use non-linear operations to transform input data and create a decision boundary in which the data can be linearly separable. An illustration of a single perceptron can be found in Figure 3. Multiple layers of these perceptrons create an MLP or neural network (Figure 4). The neural network in Figure 4 is a fully connected network. This means that each neuron receives an input from each neuron from the previous layer. For CNN, convolution layers exist within the hidden layers to perform the convolutional computations. DL aims to detect objects in a single/multiple frames similar to how humans detect and interact with objects [44]. However, the detection of pedestrians and cyclists has been a major challenge in computer vision. With the recent software and hardware advancements, there has been a real progression in this field. There are many survey papers for pedestrian detection [46; 68; 69; 70; 71; 72; 73; 74] and tracking systems, including the sensor technology and processing techniques. The use of a monocular camera for capturing images of pedestrians was used in [75]. A review of techniques of pedestrian detection techniques is compared, including some DL techniques, namely, the Convolutional Neural Network (CNN) in [43]. However, with the recent adoption of Deep Learning (DL) techniques,state-of-the-art survey for pedestrian detection and tracking using these DL techniques should be conducted [23].