YOLO v3 Real-Time Object tracking with Deep SORT
By: Rokas Balsys
Tags: Machine Learning, Data Science, Deep Learning, Computer Vision, Computer Science

Introduction

Probably we all understand that computers and algorithms are getting better every day at “thinking”, analyzing situations, and making decisions similar to humans do. Understanding computer vision is an integral part of this progress in the area of machine intelligence. 10 years ago object detection was a mystery to us, it was not even worth mentioning object tracking. These technologies have come far in that regard and the boundaries are being challenged and pushed as we talk.

While detecting objects in an image has been getting a lot of attention from the scientific community, a lesser-known and yet an area with widespread applications is tracking objects in a video or realtime video stream. That’s something that requires us to merge our knowledge of detecting objects in images with analyzing temporal information and using it to predict movement trajectories.

Object tracking might be used in sports events for example tracking basketball, catching burglars, counting passing cars in street or even track when your dog is running outside the house. There are many areas where we can use object tracking.

Object detection vs Object Tracking

If you are new in computer vision and deep learning, you may ask, what’s the difference between “Object Detection” and “Object Tracking”?

In simple terms, in object detection, we detect an object in a frame, put a bounding box or a mask around it, and classify the object. Note that, the job of the detector ends here. It processes each frame independently and identifies numerous objects in that particular frame.

Now, an object tracker on the other hand needs to track a particular object across the entire series of frames (for example video). If the detector detects 2 bananas in the frame, the object tracker has to identify the 2 separate detections and needs to track them across the subsequent frames (with the help of a unique object ID).

Challenges

Tracking an object on a straight road or a clear area might sound very easy. But hardly any real-world application is that straight forward and free of any challenges. Here I will mention some common problems that might encounter while using an object tracker.

Occlusion:

The occlusion of objects in videos is one of the most common obstacles to seamless tracking of objects. As you can see, in the above figure (left), the man in the background is detected, while the same guy goes undetected in the next frame (right). Now, the challenge for the tracker lies in identifying the same guy when he is detected in a much later frame and associating his older track and features with his trajectory.

Variations in viewpoints:

In real-life examples, the task would be to track an object across different cameras, this may be used in AI stores where there is no cashier, and we must track a customer throughout the store. A similar example is illustrated above, as a consequence of this, there will be significant changes in how we view the object in each camera. In such cases, the features used to track an object become very important as we need to make sure they are invariant to the changes in views.

Non-stationary camera:

When the camera used for tracking a particular object is also in motion for the object, it can often lead to unintended consequences. As I mentioned previously, many trackers consider the features from an object to track them. Such a tracker might fail in scenarios where the object appears different because of the camera motion (appear bigger or smaller). A robust tracker for this problem can be very helpful in important applications like object tracking drones or autonomous navigation.

Annotating training data:

One of the most annoying things about building an object tracker is getting good training data for a particular scenario. Unlike building a dataset for an object detector we can have randomly unconnected images. In object tracking, it’s required to have video or frame sequences where each instance of the object is uniquely identified for each frame.

Traditional Methods

Mean shift method:

Mean-shift or Mode seeking is a popular algorithm, which is mainly used in clustering and other related unsupervised problems. It is similar to K-Means but replaces the simple centroid technique of calculating the cluster centers with a weighted average that gives importance to points that are closer to the mean. The goal of the algorithm is to find all the modes in the given data distribution. Also, this algorithm does not require an optimum “K” value like K-Means.

Suppose we have detection for an object in the frame and we extract certain features from the detection (color, texture, histogram, etc). By applying the mean-shift algorithm, we have a general idea of where the mode of the distribution of features lies in the current state. Now when we have the next frame, where this distribution has changed due to the movement of the object in the frame, the mean-shift algorithm looks for the new largest mode and hence tracks the object.

For more info on the mean-shift, refer to here.

Optical flow method:

This method differs from mean-shift, as we do not necessarily use features extracted from the detected object. Instead, the object is tracked using the Spatio-temporal image brightness variations at a pixel level.

Here we focus on obtaining a displacement vector for the object to be tracked across the frames. Tracking with optical flow rests on four important assumptions:

Brightness consistency: Brightness around a small region is assumed to remain nearly constant, although the location of the region might change; Spatial coherence: Neighboring points in the scene typically belong to the same surface and hence typically have similar motions; Temporal persistence: Motion of a patch has a gradual change; Limited motion: Points do not move very far or randomly.

Once these criteria are satisfied, we use something called the Lucas-Kanade method to obtain an equation for the velocity of certain points to be tracked (usually these are easily detected features). Using the equation and some prediction techniques, a given object can be tracked throughout the video.

For more info on Optical flow, refer to here.

Kalman Filter

In almost any engineering problem that involves prediction in a temporal or time series sense, it can be computer vision, guidance, navigation, stabilizing systems, or even economics you may often hear “Kalman Filter”.

The core idea of a Kalman filter is to use the available detections and previous predictions to arrive at the best guess of the current state while keeping the possibility of errors in the process.

For example, now we can train a fairly good YOLOv3 object detector that detects a person. But it is not that accurate and occasionally misses detections, say 10% of frames. To effectively track and predict the next state of a person, let us assume a “Constant velocity model”. Now, once we have defined the simple model according to laws of physics, given a current detection, we can make a nice guess on where the person will be in the next frame. It all sounds perfect, in an ideal world, but there is always a noise component. So we have:

Noise associated with the constant velocity model that would follow a person. It is obvious as we cannot expect a constant velocity always. We call this “Process Noise”;

Since, the detector output, based on which we are making predictions is also not accurate, we have “Measurement Noise” associated with it.

As we see in the above pic, the Kalman filter works recursively, where we take current readings, to predict the current state, then we use the measurements and update our predictions. So, it essentially boils down to inferring a new distribution (the predictions) from the previous state distribution and the measurement distribution.

The complete mathematical formulation and derivations concerning the Kalman filter are not covered in this post. However, it is recommended for you to go through them. You can read more on this link if you have enough mathematical background.

Kalman filter works best for linear systems with Gaussian processes involved. In our case, the tracks hardly leave the linear realm and also, most processes and even noise fall into the Gaussian realm. So, the problem is suited for the use of Kalman filters.

Deep Learning based Object Tracking Approaches

Deep Regression Networks:

One of the early methods that used deep learning, for single object tracking was Deep Regression Networks (ECCV, 2016). A model is trained on a dataset consisting of videos with labeled target frames. The objective of the model is to simply track a given object from the given image crop.

To achieve this, they use a two-frame CNN architecture which uses both the current and the previous frame to accurately regress on to the object:

As shown in the above figure, we take the crop from the previous frame based on the predictions and define a “Search region” in the current frame based on that crop. Now the network is trained to regress for the object in this search region. The network architecture is simple with CNN’s followed by Fully connected layers that directly give us the bounding box coordinates.

ROLO — Recurrent Yolo

ROLO Recurrent Yolo (ISCAS 2016) — an elegant way to track objects using deep CNN. That’s a slightly modified YOLO detector with attached recurrent LSTM unit at the end, that helps to track objects by capturing the Spatio-temporal features. Here is architecture:

As shown in the above image, the architecture is quite simple. The Detections from YOLO (bounding boxes) are concatenated with the feature vector from a CNN based feature extractor (We can either re-use the YOLO backend or use a specialized feature extractor). Now, this concatenated feature vector, which represents most of the spatial information related to the current object, along with the information on the previous state is passed onto the LSTM cell.

The output of the cell now accounts for both spatial and temporal information. This simple trick of using CNN’s for feature extraction and LSTM’s for bounding box predictions gave high improvements to tracking challenges. The disadvantage of this method is that it’s quite hard to train the LSTM network.

Deep SORT

The most popular and one of the most widely used object tracking framework is Deep SORT (Simple Real-time Tracker).

In the Deep Sort tracker, the Kalman filter is a crucial component. Kalman tracking scenario is defined on the eight-dimensional state space (u,v, a,h,u’,v’, a’,h’) that contains the bounding box center position (u, v), are centers of the bounding boxes, a is the aspect ratio and h, the height of the image. The other variables are the respective velocities of the variables.

Note that variables have only absolute position and velocity factors since we are assuming a simple linear velocity model. The Kalman filter helps us factor in the noise in the detection and uses prior state in predicting a good fit for bounding boxes.

For each detection, we create a “Track”, that has all the necessary state information. It also has a parameter to track and delete tracks that had their last successful detection long back, as those objects would have left the scene. To eliminate duplicate tracks, there is a minimum number of detections threshold for the first few frames.

Now, when we have the new bounding boxes tracked from the Kalman filter, the next problem lies in associating new detections with the new predictions. Since they are processed independently here comes the assignment problem because we have no idea how to associate track_i with incoming detection_k.

To solve this, we need 2 things: A distance metric to quantify the association and an efficient algorithm to associate the data.

Deep SORT authors decided to use the squared Mahalanobis distance (effective metric when dealing with distributions) to incorporate the uncertainties from the Kalman filter. Thresholding this distance can give us a very good idea of the actual associations. This metric is more accurate than euclidean distance as we are effectively measuring the distance between 2 distributions (everything is a distribution under Kalman filter). In this case, the authors offered to use the standard Hungarian algorithm, which is a very effective and simple combinatorial optimization algorithm that solves the assignment problem.

The appearance feature vector

Up to this point, we have an object detector giving us detections, Kalman filter tracking detections, and giving us missing tracks and the Hungarian algorithm solving the association problem.

Despite the effectiveness of the Kalman filter, it fails in many of the real-world scenarios like occlusions, different viewpoints, etc.

So, to improve this, the authors of Deep SORT introduced another distance metric based on the “appearance” of the object.

So, authors first built a classifier over the dataset, trained it till it achieved a reasonably good accuracy, and then strip the final classification layer. Assuming a classical architecture, we will be left with a dense layer producing a single feature vector, waiting to be classified. That feature vector becomes our “appearance descriptor” of the object.

Overview of the CNN architecture. The final batch and `2 normalization projects feature onto the unit hypersphere

The “Dense 10” layer shown in the above pic will be our appearance feature vector for the given crop. Once trained, we just need to pass all the crops of the detected bounding box from the image to this network and obtain the “128 X 1” dimensional feature vector.

The updated distance metric will be:

D=Lambda∗Dk+(1−Lambda)∗Da

Where Dk is the Mahalanobis distance and Da is the cosine distance between the appearance feature vectors and Lambda is the weighting factor.

A simple distance metric, combined with a powerful deep learning technique is all it took for deep SORT to be an elegant and one of the most widespread Object trackers.

Code Implementation:

In this tutorial part, I will implement our generic object tracker on pre-trained (trained on COCO dataset) YOLOv3 model.

First, you should clone my GitHub repository and follow the setup instructions from the same repository REAMDE.md file. If you have issues, follow my past YOLOv3 tutorial (links can be found at the end of this tutorial).

Now, since we are interested in creating an object tracker and not a detector, we shall use a video or real-time camera detection outputs, to receive a series of frames.

The deep_sort folder in the repo has the original deep sort implementation, complete with the Kalman filter, Hungarian algorithm, and feature extractor. I had to modify original files slightly to make it work with TensorFlow 2, originally it was written to work with TensorFlow 1.x.

Let’s look at my object_tracker.py script:

Same as in object detector, first we initialize our YOLOv3 model, then we import necessary functions for our deep SORT tracker.

Differently from my other functions in this project, which I put in yolov3/utils.py script, now I create Object_tracking in the same file. We call this function in the following way:

Object_tracking(yolo, video_path, ‘’, input_size=input_size, show=True, iou_threshold=0.1, rectangle_colors=(255,0,0), Track_only = [])

Where:

yolo is our YOLOv3 model;

video_path is our video path, if there is no path, it will use a web camera;

input_size, show, iou_threshold=0.1, rectangle_colors are obvious parameters, which don’t need an explanation, otherwise, check my past tutorials;

Track_only is used to define specific objects to track, for example, Track_only = [“person”]. Then our tracker will track only persons.

Talking about the Tracking, model_filename = ‘model_data/mars-small128.pb’ loads a CNN pre-trained deep_SORT tracking model. The function “ tracker=Tracker(metric) “ is one of the most important elements. tracker is an object of the class Tracker defined in the deep_sort library, that takes care of creation, keeping track and eventual deletion of all tracks.

I am not going to explain code step by step, only at the abstract level. Deep SORT Detection requires boxes input in the following format (x, y, width, height), but our YOLOv3 outputs detections as (x_min, y_min, x_max, y_max), so we do a conversion.

The whole tracking is done in the following few lines:

# Pass detections to the deepsort object and obtain the track information. tracker.predict()

tracker.update(detections)

In other lines, we do operations with data.

We can look into object tracking example (Tracking only the people), where I test the deep_sort framework on the test.mp4 video:

This is only a short gif, from my results, if you want to see more, check my YouTube tutorial above.

Training custom feature extractor:

Since, the original deep sort focused on the MARS dataset, which is based on people, the feature extractor is trained on humans, this means that this object tracking model works best to track people. It doesn’t mean that our object tracker won’t work on custom object detector, simply it won’t work that good as it could. To get the best out of this tracking method, you should train an equivalent feature extractor for your custom objects.

But in this tutorial, I am not covering the training part. To train the deep association metric model paper authors on their GitHub give a reference to https://github.com/nwojke/cosine_metric_learning approach which is provided as a separate repository.

Conclusion:

I hope this tutorial post has helped you to gain a comprehensive understanding of the core ideas and components in object tracking. Also, I gave a fully working code, to test object tracking by your self, that you might use for your own project. I have just one request, if you try this code on your project, just mention me in your project. Also, it would be interesting to see, what problem to solve you use it, so don’t hesitate to comment on this post, on YouTube, or even write me an email! Now it’s enough reading, go to my GitHub, clone repository, and try everything by yourself!

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