Interpretation of the concept of target tracking in computer vision

Object tracking is an important task in computer vision and is widely used in traffic monitoring, robotics, medical imaging, automatic vehicle tracking and other fields. It uses deep learning methods to predict or estimate the position of the target object in each consecutive frame in the video after determining the initial position of the target object. Object tracking has a wide range of applications in real life and is of great significance in the field of computer vision.

Object tracking usually involves the process of object detection. Here is a brief overview of the object tracking steps:

1. Object detection, where the algorithm classifies and detects objects by creating bounding boxes around them.

2. Assign a unique identification (ID) to each object.

3. Track the movement of detected objects in frames while storing relevant information.

Types of target tracking

There are two types of target tracking: image tracking and video tracking.

Image tracking

Image tracking is the task of automatically identifying and tracking images. Mainly used in the field of augmented reality (AR). For example, when fed a 2D image through a camera, the algorithm detects a 2D planar image, which can then be used to overlay 3D graphic objects.

Video tracking

Video tracking is the task of tracking moving objects in videos. The idea of ​​video tracking is to associate or establish a relationship between a target object as it appears in each video frame. In other words, video tracking analyzes video frames sequentially and splices an object’s past location with its current location by predicting and creating bounding boxes around it.

Video tracking is widely used in traffic monitoring, self-driving cars, and security because it can process real-time footage.

The 4 stages of the target tracking process

Phase 1: Target initialization

Involves definition object or goal. Combined with the process of drawing a bounding box around the initial frame of the video. The tracker must then estimate or predict the object's position in the remaining frames while drawing bounding boxes.

Phase Two: Appearance Modeling

Appearance modeling involves modeling the visual appearance of an object. When the target object passes through various scenarios such as lighting conditions, angles, speeds, etc., it may change the appearance of the object and may cause error information and the algorithm to lose tracking of the object. Appearance modeling is therefore necessary so that the modeling algorithm can capture the various changes and distortions introduced when the target object moves.

Appearance modeling consists of two parts:

• Visual representation: It focuses on building robust features and representations that can describe objects
• Statistical Modeling: It uses statistical learning techniques to efficiently build mathematical models for object recognition.

Phase Three: Motion Estimation

Motion estimation typically extrapolates the predictive capabilities of the model to accurately predict the future location of an object.

Phase Four: Target Localization

Once the location of the object is approximated, we can use the visual model to lock on to the exact location of the target.

Object Tracking Levels

Object tracking can be defined as two levels:

Single Target Tracking (SOT)

Single Object Tracking (SOT) is designed to track a single class of objects rather than multiple objects. Sometimes called visual object tracking. In SOT, the bounding box of the target object is defined in the first frame. The goal of this algorithm is to locate the same object in the remaining frames.

SOT falls into the category of detection-free tracking because the first bounding box must be provided manually to the tracker. This means that a single object tracker should be able to track any object given, even objects for which no classification model is available for training.

Multiple Object Tracking (MOT)

Multiple Object Tracking (MOT) refers to a method in which a tracking algorithm tracks each individual object of interest in a video. Initially, the tracking algorithm determines the number of objects in each frame and then tracks the identity of each object from one frame to the next until they leave the frame.

Target tracking method based on deep learning

Target tracking has introduced many methods to improve the accuracy of tracking models sex and efficiency. Some methods involve classic machine learning methods such as k-nearest neighbors or support vector machines. Below we discuss some deep learning algorithms for target tracking tasks.

MDNet

A target tracking algorithm that utilizes large-scale data for training. MDNet consists of pre-training and online visual tracking.

Pre-training: In pre-training, the network needs to learn multi-domain representations. To achieve this goal, the algorithm is trained on multiple annotated videos to learn representations and spatial features.

Online visual tracking: Once pre-training is completed, domain-specific layers are removed, leaving the network with only shared layers containing the learned representations. During inference, a binary classification layer is added, which is trained or fine-tuned online.

This technique saves time, and it has proven to be an effective online-based tracking algorithm.

GOTURN

The deep regression network is a model based on offline training. The algorithm learns a general relationship between object motion and appearance and can be used to track objects that do not appear in the training set.

Generic Object Tracking using Regression Networks or GOTURN uses a regression-based approach to track objects. Essentially, they regress directly to locate the target object in only one feedforward pass through the network. The network accepts two inputs: the search area of ​​the current frame and the target of the previous frame. The network then compares these images to find the target object in the current image.

ROLO

##ROLO is a combination of recurrent neural network and YOLO. Generally, LSTM is more suitable to be used in conjunction with CNN.

ROLO combines two neural networks: one is CNN, used to extract spatial information; the other is LSTM network, used to find the trajectory of target objects. At each time step, spatial information is extracted and sent to the LSTM, which then returns the location of the tracked object.

DeepSORT

DeepSORT is one of the most popular target tracking algorithms and is an extension of SORT.

SORT is an online-based tracking algorithm that uses a Kalman filter to estimate the position of an object given its previous position. The Kalman filter is very effective against occlusions.

After understanding SORT, we can combine deep learning technology to enhance the SORT algorithm. Deep neural networks allow SORT to estimate the location of objects with greater accuracy because these networks can now describe the characteristics of the target image.

SiamMask

is designed to improve the offline training process of fully convolutional Siamese networks. The Siamese network accepts two inputs: a cropped image and a larger search image to obtain a dense spatial feature representation.

The Siamese network produces an output that measures the similarity of two input images and determines whether the same object is present in both images. By increasing the loss using binary segmentation tasks, this framework is very effective for object tracking.

JDE

JDE is a single-shot detector designed to solve multi-task learning problems. JDE learns object detection and appearance embedding in a shared model.

JDE uses Darknet-53 as the backbone to obtain feature representation at each layer. These feature representations are then fused using upsampling and residual connections. A prediction header is then appended on top of the fused feature representation, resulting in a dense prediction map. To perform object tracking, JDE generates bounding box classes and appearance embeddings from the prediction head. These appearance embeddings are compared to embeddings of previously detected objects using an affinity matrix.

Tracktor

Tracktor is an online tracking algorithm. It uses object detection methods to perform tracking by training a neural network only on the detection task. Essentially predicting the location of the object in the next frame by computing a bounding box regression. It does not perform any training or optimization on the tracking data.

Tracktor’s object detector is usually Faster R-CNN with 101 layers of ResNet and FPN. It uses the regression branch of Faster R-CNN to extract features from the current frame.

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