Visual Object Tracking Using Siam and TensorFlow as Hybrid Model in AI

Visual object tracking is a bit complicated task in computer vision because it involves tracking objects as per the interest with pattern recognition. The motive of object tracking is to project the trajectory of the object along with the position by facing various challenges such as variations in lighting condition, fast motions, getting obstacles over the objects, etc. Tracking algorithm usually works in that manner where it initializes the target object in the very first frame along with its location by using coordinates and other pertinent properties continually in the following frames. It also includes feature extraction, object visualization, pattern following and model prediction. Numerous fields, such as robotics, autonomous navigation, augmented reality, video analysis, surveillance, and human-computer interaction, use visual object tracking extensively [1-3].

It may be used, for example, to guide unmanned aerial vehicles, monitor people and objects in congested areas, analyze sporting events, improve immersive gaming, and provide assistive devices for the blind. Visual object tracking has come a long way in recent years, but it is still a difficult challenge since real-world scenarios are inherently complex and need balancing accuracy, efficiency, and resilience. In order to push the limits of object tracking, researchers are still creating and improving edge tracking algorithms by utilizing developments in deep learning, probabilistic modeling, optimization strategies, and sensor technologies [4].

Several methods are used in visual object tracking to precisely track and identify things throughout a series of frames in a video. Feature extraction techniques, appearance descriptors or keypoints, are commonly employed in these procedures to extract unique attributes from the target object. Motion estimation techniques compute the movement of an item in between frames, enabling placement that is predicted. To capture the item's spatial extent, object representation methods like bounding boxes or pixel-wise masks are used. Similarity metrics assess how an object appears or is composed differently in different frames. To ensure stable and dependable tracking performance, tracking algorithms frequently include techniques for managing occlusions, scale changes, and other difficulties that are frequently encountered in real-world circumstances [5]. The compact visibility can observe several difficulties that work faces during tracking an object using pattern recognition or the features present in the image. It is also required to find out the background of the image to properly segment the foreground for better precision and accuracy.

Figure 1 shows a desk with gadgets, where a yellow box highlights one object to demonstrate object tracking.

TensorFlow makes it possible to use deep learning-based algorithms, which makes visual tracking easier. First, target objects for tracking are defined by tagged datasets of pictures or videos. TensorFlow provides a range of pre-trained models, such as SSD and Faster R-CNN, or uses its high-level APIs, such as TensorFlow Keras to enable the development of bespoke models. The prepared datasets are used to train these models, improving their capacity to forecast bounding boxes around tracked objects with precision. The trained models analyze fresh frames and produce predictions for object positions during inference. These predictions can be improved by post-processing methods like smoothing trajectories or removing false positives. Ultimately, the bounding boxes or monitored item coordinates are included into more expansive systems for further examination or utilization.

1.png

Figure 1. Visual object tracking [6]

This research proposes a hybrid network based on Siamese and TensorFlow, designed for offline processing with large datasets. The network comprises multiple sub-networks for feature extraction, regression, and classification. By integrating the Siamese Network with TensorFlow, an object detection approach, the feature extraction model is enhanced for improved visual tracking analysis. Unlike previous recognition frameworks that repurpose classifiers or localizers for feature extraction, this model applies features across multiple areas of an image, even when objects are scaled. The system will undergo testing with various datasets and benchmarks, including OTB50, OTB100, and TempleColor128, aiming to achieve higher levels of accuracy.

3.1 Problem definition

Even though visual object tracking has advanced significantly consideration to methods like deep learning regression models and reinforcement learning a number of significant obstacles still exist. Among these are the challenges of preserving robustness in the face of unfavorable circumstances like motion blur, low resolution, fluctuating lighting and object deformation. The real-world applicability of many current methods is limited by their struggles with boundary effects occlusion and generalization across diverse datasets. Furthermore, intricate designs are frequently unsuitable for real-time applications and necessitate extensive tuning. Effective mechanisms to manage long-term dependencies and update models effectively during tracking are also lacking. Tracking accuracy and speed balance is still a big concern which emphasizes the need for more flexible lightweight and dependable tracking systems that can function well in a variety of situations. 

3.1.1 Method design

To precisely localize the target object in every frame the suggested method design for visual object tracking combines a lightweight regression framework with a feature extraction module based on Convolutional Neural Networks (CNNs). First input frames undergo preprocessing, which includes normalization and resizing and a region of interest is chosen for effective calculation. Selected CNN layers that strike a balance between semantic richness and spatial detail are used to extract deep features. After that the target bounding box is predicted by a regression-based tracking module. To account for variations in appearance scale and occlusion, an adaptive update strategy uses confidence-based online learning to improve the model. A re-detection mechanism is incorporated for tracking failure recovery and frame-wise predictions are smoothed to improve temporal consistency. Stochastic gradient descent is used to optimize the model after it has been trained using a combination of regression and similarity-based loss functions. Metrics like precision AUC, and success rate are used to assess the model's robustness and real-time capability on benchmark datasets like OTB VOT, and LaSOT.

3.1.2 Experimental validation

To ensure a thorough performance evaluation under a variety of real-world scenarios the proposed visual object tracking system was experimentally validated on well-known benchmark datasets such as OTB-2013 OTB-2015 VOT LaSOT and UAV123. Standard metrics like accuracy success rate intersection over union (IoU) and area under the curve (AUC) were used to gauge the systems performance. To demonstrate gains in robustness, adaptability and tracking accuracy especially in the face of difficult circumstances like motion blur occlusion scale variation and illumination changes a comparative analysis was conducted against a number of cutting-edge trackers. Across sequences with different object classes motion patterns and scene complexities the suggested method continuously outperformed baseline approaches exhibiting superior tracking stability and efficiency while preserving real-time performance with little computational overhead.

3.2 Siamese network

Common applications of the Siamese neural network include object tracking and similarity learning. The fundamental design consists of two similar neural networks with the same topology and weights, often referred to as Siamese twins or twin networks. The Siamese neural network may be represented mathematically in the following way:

Let $f(x ; \theta)$ stands for the function that the neural network learnt, where x is the input and θ is the network's parameters (weights and biases). MThe modelhas two identical networks sharing the same parameters for a Siamese Network. Let's say that the inputs to the first and second networks are represented by the symbols x₁ and x₂, respectively. The output of each network is a feature vector, denoted as $f\left(x_1 ; \theta\right)$ and $f\left(x_2 ; \theta\right)$ espectively. In other words, the following mathematical representation captures the fundamental structure of a Siamese Neural network:

$f\left(x_1 ; \theta\right)=\operatorname{Net}\left(x_1 ; \theta\right)$              (1)

$f\left(x_2 ; \theta\right)=\operatorname{Net}\left(x_2 ; \theta\right)$              (2)

$S\left(x_1, x_2\right)=$ Similarity $f\left(x_1 ; \theta\right), f\left(x_2 ; \theta\right)$              (3)

where, $f\left(x_1 ; \theta\right)$ is the feature vector of first input with the network parameter $\theta$, similarly for the $f\left(x_2 ; \theta\right) . S\left(x_1, x_2\right)$ is the similarity between the first input vector and the second one.

In actuality, depending on the particular job and application, the network design, loss function, and optimization technique may change. Additionally, Siamese Networks are frequently trained for similarity learning tasks using methods like contrastive loss and triplet loss. In order to extract image characteristics, the Siamese Network uses the categorization network, which biases the retrieved features toward semantic information. SiameseRPN++ uses ResNet as its feature extraction backbone network. The findings of the experiment confirm that various channels respond differently to distinct object categories, suggesting that deep features can capture semantic information associated with object prejudice. The Siamese Network's dual-branch structure allows it to interpret input picture data in a different way, producing characteristics that take various dimensions, such as channel and space, into account. By using the attention mechanism to filter visual data, the network is able to determine the object's significance during feature extraction, highlighting target features that are pertinent to the tracking job and ignoring background information. The most adorable feature of Siamese Network is offline learning approach. It trains the model in offline mode and test in the same manner. The template branch and the detection branch are the two branches that make up the Siamese tracker. While the detection branch examines the target image patch from the current frame, the template branch receives the target image patch from the previous frame as input. To track an object, first of all, it is required to preprocess the image using smart filters and localize the image by inputting the target object in the very first frame. Figure 2 illustrates the box marks target object initialization in object tracking selecting the ball as the focus to follow in the video.

2.png

Figure 2. Target object initialization

3.png

Figure 3. Color mapping of target feature object

The process of extracting features began when the target object was established. Using a set of pixels with comparable spectral, spatial, and/or textural qualities, an object (or segment) is used in feature extraction, an object-based method for classifying images. Figure 3 shows the color mapping highlights the ball’s features to initialize it as the tracking target.

On the other hand, conventional classification techniques are pixel-based, categorizing images based on the spectral information of individual pixels.

4.png

Figure 4. Task specific model training

Once the feature extraction has been done, then feature training will be started, and with the help of Siamese Network; task-specific training will be accomplished and generate the output model. Figure 4 illustrates task-specific model training in object tracking.

3.3 TensorFlow

For object identification tasks, TensorFlow is a powerful tool with an extensive ecosystem of models and tools. Choosing an appropriate model architecture, such as Faster R-CNN, SSD, or YOLO, which each have their trade-offs between speed and accuracy, is usually the first step in the process.

Convolutional neural networks (CNNs) are used by these designs to forecast bounding boxes and class labels for objects that are recognized, as well as to extract characteristics from input photos. Using hierarchical features that capture semantic information about the objects in the image, CNNs process the input image. Convolutional and pooling layers, which gradually decrease the spatial dimensions while increasing the depth of feature maps, are commonly used to achieve this. The model predicts bounding boxes, or coordinates, that closely surround items of interest inside the picture once features are retrieved. Figure 5 shows the main steps in TensorFlow object detection: extracting features, finding and classifying objects, then filtering results. Regressing the coordinates of a group of predetermined anchor boxes to better suit the position of the item is a common method for doing this. At the same time, the model classifies objects by giving each bounding box a class probability that indicates how likely it is to include a certain item category. In order to compute class probabilities, this is often accomplished using extra convolutional layers and a softmax activation function. Non-Maximum Suppression is used to filter out overlapping bounding boxes with lower confidence ratings in order to get rid of redundant detections. This guarantees the retention of just the most certain detections. Three phases are usually included in the object detection operation:

(a) The way that the input is divided into manageable chunks. Figure 6 demonstrates image segmentation: the grid divides the cat photo into distinct regions, helping separate and analyze parts of the image for tasks like object recognition. The whole image is covered by the extensive collection of bounding boxes, as shown.

(b) For each segmented rectangular area, feature extraction is conducted to determine whether the rectangle contains a valid object. Each box shows a region where features are extracted for later recognition or tracking is presented in Figure 7. Figure 8 depicts Non-Maximum Suppression merges overlapping detection boxes into one, creating a single rectangle around the cat.

Non-Maximum Suppression creates a single boundary rectangle by joining overlapping boxes.

5.png

Figure 5. Visual object detection in TensorFlow

6.png

Figure 6. Segmentation of an image

7.png

Figure 7. Feature extraction of all boxes

8.png

Figure 8. Object detection using non-max suppression

3.4 Architecture of Siamese Network

Siamese Networks are a subclass of networks defined by the use of two sub-networks that are exactly the same for one-shot classification tasks. Even though these sub-networks analyze distinct inputs, they retain the same designs, parameters, and weights. Siamese Networks learn a similarity function, in contrast to typical CNNs that are trained on large datasets to predict various classes. They can distinguish between classes with less data thanks to this feature, which makes them quite effective for one-shot classification tasks. These networks are frequently able to categorize pictures effectively with only one sample thanks to their amazing feature. Instead of using a lot of labeled data for training as in the standard technique, few-shot learning trains models to make predictions using only a limited number of instances. When it becomes difficult or expensive to gather large amounts of labeled data, the value of few-shot learning becomes clear. Few-shot models may produce predictions with little input sometimes even from a single example because of its design, which captures the intricacies seen in a tiny sample size. This feature is made possible by several design processes including Siamese Networks, Meta-learning, and related techniques. With the help of these frameworks, the model is able to derive insightful data representations and apply them successfully to new, untested samples.

The goal of the differencing layer is to draw attention to the differences between dissimilar pairs while highlighting the commonalities between inputs. The Euclidean Distance function is employed to do:

Distance $\left(x_1, x_2\right)=\left\|\mathrm{f}\left(x_1\right)-\mathrm{f}\left(x_2\right)\right\|_2$          (4)

Here, x₁ and x₂ represent the two inputs, Encoding (x₁) and Encoding(x₂) denote the output of the encoding function, and Distance represents the distance function. The entropy loss can be encountered as:

$-(y \log (p)+(1-y) \log (1-p))$               (5)

where,

y represents the true label,

p represents the predicted probability

$K(i, j)=\frac{\sum_{x=1}^N \sum_{y=1}^N(x, y) \times T(x, y)}{\sqrt{\sum_{x=1}^N \sum_{y=1}^N[S(x, y)]^2} \sqrt{\sum_{x=1}^N \sum_{y=1}^N[T(x, y)]^2}}$               (6)

where,

T(x,y) denotes the template image,

S(x,y) represents the search region of the target, and

K indicates the height and width of the data.

The response map is obtained by using a fully-convolutional Siamese Network, as shown in the flow chart of the Siamese framework. The re-detection network is initiated if the secondary peak in this map is more than 0.75 times the size of the main peak.

3.5 Process model

To maximize input quality, the system first gathers frames for preprocessing. The networks are obtained and loaded to recognize objects based just on their appearance once all preprocessing activities have been completed. Figure 9 shows a Siamese network architecture for object detection, where parallel networks compare a template and a detection frame to localize and track the target object.

9.png

Figure 9. Object detection Siamese Network architecture

After that, an additional network is utilized for effective object tracking. The system makes use of two distinct strategies that are well-known for their effectiveness in tracking and object identification, which helps to improve the regression model. This minimizes overlap counts and increases frame rate per second while maintaining excellent system efficiency. Figure 10 represented the proposed flow chart.

10.png

Figure 10. Proposed flow chart

First, frames from a dataset are entered and converted from RGB to grayscale using the Siamese Network and TensorFlow technique. After that, it creates frame sequences and specifies the target region or object in accordance with the dataset criteria. In order to start the detection procedure, the algorithm loads the TensorFlow and Siamese Network models. It looks for the target item inside the frames throughout each cycle. It counts successfully regressed items and increases the count for overlapping detections if the detection confidence is greater than a predetermined threshold. The target object's width, height, and (x, y) coordinates are extracted by the method. It then evaluates accuracy by contrasting these extracted coordinates with the ground truth. Lastly, it uses counts of loss and true regression to calculate the accuracy of the system before terminating the algorithm. To guarantee efficient object tracking the Siamese Network and TensorFlow hybrid tracking model was put into practice with particular training setups and parameters. To filter and improve detection results Non-Maximum Suppression (NMS) was applied with a confidence threshold of 0. 5 and an IoU threshold of 0. 4. The Adam optimizer was used to optimize the Contrastive Loss function over 50 epochs with a batch size of 32. The learning rate was set at 1e-4. It used a modified AlexNet as the feature embedding backbone and standardized the input images to 127×127 for the exemplar (template) and 255×255 for the search region. The algorithmic flow was made more understandable by adding a pseudocode representation that detailed the steps involved from feature extraction and similarity calculation to score thresholding and final bounding box selection. All of these specifics improve the suggested methods transparency and reproducibility.