# Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey
## 3 Detection Techniques: A Brief History  
Detection techniques, especially for pedestrians, has been widely researched with several techniques. The first instance of object detection is known as the region of interest (ROI) [28]. Once the potential location of the desired object (i.e., pedestrian or cyclist) is identified in an image, feature extraction takes place. These features can include edges, shapes, curvature, etc. These features are sent to a classifier for classification [28] (see Figure 1). The Background Subtraction (BS) approach was the first technique applied for detecting a moving object. In this approach, the moving objects are identified by comparing the current frame with the reference frame, known as the background image [23; 29]. This method is simple to implement but is susceptible to environmental conditions such as light intensities (i.e., time of day, shadowing) and dynamic backgrounds [30]. To improve the detection and tracking, a number of advanced techniques such as the sliding window, objectiveness and selective search were developed [31]. Algorithms for feature extraction and classification for object detection can be either hand-crafted or DL-based methods. Hand-crafted methods for feature extraction are based on models that were manually designed on low-level features to propose ROIs [19]. These models were based on techniques such as BS, the histogram of oriented gradients (HOG) features [32; 33] or local binary pattern (LBP) [34]. Hand-crafted methods can be limited and not very robust as complex features can be difficult to hand-craft. DL techniques allow the network to determine features. This can provide a higher level of abstraction. Then classifiers, such as a Support Vector Machine (SVM) [19; 35; 36; 37; 38], a decision tree [19; 36; 37; 38] or a deep network [39; 40] are used to classify the object (e.g., pedestrian, cyclist) in the image or video sequence. Deep networks have shown promising results in pedestrian detection, outperforming some traditional methods of pedestrian detection. DL-based techniques will be discussed in later sections. Some of the more commonly used hand-crafted techniques for pedestrian and cyclist detection are discussed below. Haar-like features detect the changes in intensities in the horizontal, vertical and diagonal directions to detect the object [23; 41]. [PERSON] and [PERSON] ([PERSON]) implemented the Haar-like features detection approach, while also taking into account the intensity information from the videoframe [30; 42]. Introduced in 2003, it used the sudden changes in pixel intensities to detect the shape of an object [42; 43; 44]. The VJ detector was one of the earlier techniques designed for pedestrian detection [42]. It used box-shaped filters for feature extraction which is then fed into a classifier based on adaptive boosting known as AdaBoost [45]. [PERSON] and [PERSON] presented the HOG (detector which uses a linear SVM for classification [25; 43; 44; 25]). The HOG detector finds an object's shape and appearance based on the intensities of the local gradients or the orientation of the edge [23; 32]. The HOG detector became a building block for the Deformable Part Model (DPM) detector in later works [25; 44; 46; 47]. DPM was used to weaken the effects of deformation of non-rigid objects [48]. DPM is a popular method for object detection and works well with varying and occluded appearances [48; 49]. Based on the DPM, many other object detection methods have been proposed [50]. DPM was implemented in [25] to simultaneously detect and classify both pedestrians and cyclists using an innovative detection approach with a deep network for classification and localisation. The detection method, upper body-multiple potential regions (UB-MPR ), focused on the UB of the pedestrian/cyclist for object candidate abstraction as the UB of these road users are normally similar and visible. The potential object regions were extracted using multiple potential regions (MPR) for the UB of the candidate.