## 3 Obstacle Avoidance Scenario
### Optical Flow Based  
The optical flow-based method uses camera images frame-by-frame, and monitor each frame's pixel-level movement to find the motion and temporal variation in each image's grayscale version. [PERSON] et al. [269] used the optical flow method, where they used two-frame pixel matching to detect an obstacle in real time. Using optical flow information,Souhila and [PERSON] [167] developed an algorithm that can locate any obstacle by detecting any change from the data. [PERSON] et al. [270] developed an algorithm to find the obstacle edges and their changes from the optical flow images. [PERSON] and [PERSON] [271] proposed an algorithm that used optical flow with point-tracking algorithms to detect stationary and moving obstacles and show a safe path. [PERSON] et al. [272] used two sides of the optical flow images to generate the turn rate of a UAV through the urban canyon. The kernelized correlation filter (KCF) framework, proposed by [PERSON] et al. [273], uses an adaptive obstacle detection and tracking approach. [PERSON] et al. [274] used a sequence of images using sparse optical flow to generate an artificial potential field or visual potential field. Even though this method can be applied to any unknown environment, it cannot identify the obstacle's specific characterization. In addition, it renders poor performance when applied to stationary and relatively slow-moving obstacles.  
Stereo-vision, also known as binocular stereo-vision, uses multiple camera feeds from different angles to generate depth and detect stereo-vision-based obstacles [275]. Due to its ability to provide more accurate data than other visionary detection systems, several research studies have been conducted on stereo-vision-based object tracking. Using the simplified stereo-vision algorithm, [PERSON] et al. [276] detected vehicle identification and distance. [PERSON] et al. [277] presented a stereo-vision-based obstacle detection method that reconstructs and works on the 3D points matching of the object edges. [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [168] used two rigidly mounted cameras in a coaxial stereo configuration to capture stereo images of the environment and process them for navigation. [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [169] used a 3D camera to produce depth information and converted it into the UV-disparity domain, which presents obstacles and ground surfaces as lines. [PERSON] and [PERSON] [170] proposed an algorithm using stereo-vision and clustering data to localize obstacles. [PERSON] and [PERSON] [171] used an RGB-D camera to generate the 3D surface of the surroundings, generating a radial function for every obstacle, and then creating a safe UAV path. [PERSON] et al. [278] proposed a novel insulator detection approach based on RGB-D saliency detection and structural feature searching for aerial images captured by a UAV power transmission line inspection system.  
Stereo-vision works like a human eye, which captures an object's view and assumes the distance. The binocular stereo-vision detection system needs a massive amount of data to process the figure, and real-time processing requires a powerful system [54; 275; 279; 280].