A complete guide to AI object tracking with DeepSORT

When it comes to a computer vision task, it involves identifying as well as locating objects in videos and images. Therefore, it becomes a relevant part of different applications including robotics, self-driving cars, & others. Object tracking can be defined as the process of tracking detected objects throughout frames harnessing their temporal & spatial features. In this blogpost, we are going to delineate clear information about the way DeepSort works. In this context, we need to comprehend the elements of object tracking and the relevant innovations for DeepSORT. The popular object tracking algorithms involve SORT and DeepSORT with YOLO-NAS. Additionally, we will explore how YOLO-NAS for object tracking can be used for custom datasets.

Surveillance cameras play a vital role in securing our business atmosphere and home. For real time monitoring, computer vision and AI can be used to detect objects through frames.

Introduction to tracking

Deep learning tracking is delineated as the task of predicting the varied positions of objects throughout the entire video utilizing their temporal & spatial features. Tracking is acquiring the initial set of detections & tracking them throughout video frames at the time of maintaining the assigned ids.

Object tracking has several benefits and real-world applications. 

• Sports verticals

Tracking can be used in different sports for tracking the movement of balls & players.

• Traffic monitoring

Tracking is used for monitoring traffic and tracking vehicles movement on the road. It is highly beneficial for evaluating traffic & detecting vehicles.

Different types of trackers

Two major categories of trackers involve single and multiple object trackers. 

1. Single object tracker: The single object tracker has the ability to track a single object regardless of the presence of multiple objects present in the frame. They perform by firstly initializing the location of the object in the first frame and tracking it throughout the sequence of frames.
2. Multiple object tracker:  Multiple object trackers can easily keep track of different objects present inside the frame. These trackers are trained on a large amount of data.

SORT & DeepSORT object tracking algorithms

SORT is considered as an approach that tracks objects and comprises four key elements comprising estimation, detection, creation & deletion and data association of track identities. SORT has great performance when it comes to tracking precision and accuracy however, it includes certain restrictions.

SORT algorithm returns tracks with a high number of identity switches. Among the limitations involve its inability to confront association matrices as whenever it is used it fails.

Introducing DeepSORT

DeepSORT can be defined as a multiple object tracking algorithm that enhances the accuracy and efficiency of a DeepSORT system. It can recognize and track objects by applying an advanced association metric that integrates both motion and appearance descriptors. This integration reduces the identity switches promoting a more efficient tracking mechanism. It can stand out as a brief tracking algorithm in computer vision.

Object detection with YOLO-NAS

YOLO-NAS is a cutting-edge object detection model that leverages the power of AutoNAC. AutoNAC is Deci’s proprietary Neural Architecture Search technology that integrates the recent deep learning advancements to embellish relevant aspects of existing YOLO models comprising quantization support & accuracy-latency tradeoff. It offers an innovative approach to ensure superior performance providing accuracy-latency and quantization support tradeoffs in object detection. It makes the model more accessible for real-time edge device applications.

Tools required for object tracking with DeepSORT 

For this project requirement, Google Colab is harnessed to provide a compact 15 GB graphics card. It's time to explore the various software tools we will utilize for implementing YOLO-NAS with SORT & DeepSORT tracking in Python.

Simple online real time tracking with object tracking

The SORT algorithm can estimate the location of an object depending on its previous location. This method uses Kalman filters.

Deep simple online real-time tracking

Deep SORT can seamlessly combine a deep learning-based object detector with a tracking algorithm for accomplishing high accuracy and robustness in complicated environments.

SuperGradients 

SuperGradients is Deci’s PyTorch-based and open-source computer vision training library. The YOLO-NAS model is easily accessible under an open-source license for use on SuperGradients.

Stepwise guide on object tracking 

Let's explore the stepwise guide on the ways to implement object tracking with YOLO-NAS and SORT/DeepSORT algorithms.

Step 1: Installation of required changes

The primordial step is to install the important changes which involve SuperGradients and the Filterpy. YOLO-NAS is included in the Supergradient’s package. It is majorly required to perform object detection on videos and images. Contrarily, for doing object tracking with the SORT algorithm, the Filterpy is required.

Subsequent to installing the SuperGradients and Filterpy packages, ensure to restart the runtime.

Step 2: Importing required libraries

Subsequent to installing the essential packages we will import the important libraries comprising supergradients, opencv and numpy.  As YOLO NAS comprises a super gradients package, we will import models from the supergradients training library which offers access to the pretrained YOLO NAS model weights.

Step 3: Downloading the pretrained YOLO-NAS Model weights

As we have installed all the relevant packages as well as imported required libraries, our developers are going to download the pre-trained YOLO-NAS model weights. It comes in different model sizes and is the fastest and less accurate than other models. For instance, we are going to utilize the medium and large rendition of YOLO-NAS models.

Step 4: Object Detection with YOLO-NAS on Video

In order to perform object detection on a video, the developers are going to loop through each video frame individually for performing the detection of objects in each frame. It utilizes openCV for writing functions.

Step 5: Expert object tracking with YOLO-NAS and DeepSORT

Within an object tracking process, a unique ID is assigned to each of detected objects. For implementing this, we acquire a complete set of detections, as experts assign a unique ID to each object detected and track them throughout the frames of the video feed at the time of maintaining the assigned IDs.

Process of downloading DeepSORT files

DeepSORT files are downloaded from the drive into the Google Colab notebook.

Initialization of DeepSORT

The following step involves the initialization of DeepSORT files downloaded from the drive.

Step 6:  Vehicles counting with YOLO-NAS & SORT object tracking

Harnessing the potential of object detection and tracking, our developers are going to create a vehicle counting application that can easily track as well as count the number of vehicles entering and exiting the lane. For the purpose of object detection with the SORT algorithm, we can download the sort.py file from the drive into the Google Colab notebook.

Step 7: Vehicles counting with YOLO-NAS & DeepSORT object tracking

In the previous phase we explored the vehicles counting system with YOLO-NAS and SORT object tracking. As mentioned earlier DeepSORT is easily initialized and created with helper functions. To know more about the mainscript versions connect with our professionals.

Final Conclusion

After observing the varied aspects of DeepSORT and SORT in addition to YOLO-NAS, it can be concluded that YOLO-NAS is a great instance of advanced foundation models developed by Deci AI. While selecting a foundation or custom model, you can train or fine tune it by using your own data through Deci’s SuperGradients. In order to amplify the model’s efficiency and inference speed Deci’s Infery SDK is an important tool. Connect with our expert individuals at Nextbrain to know more about Deepsort.