# Tracking-by-Self Detection: A Self-supervised Framework for Multiple Animal Tracking

C. B. Dev Narayan, Fayaz Rahman, Mohib Ullah, Faouzi Alaya Cheikh, Ali Shariq Imran, Christopher Coello, Øyvind Nordbø, G. Santhosh Kumar, Madhu S. Nair

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## To cite this version:

C. B. Dev Narayan, Fayaz Rahman, Mohib Ullah, Faouzi Alaya Cheikh, Ali Shariq Imran, et al. Tracking-by-Self Detection: A Self-supervised Framework for Multiple Animal Tracking. 19th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2023, León, Spain. pp.561-572, 10.1007/978-3-031-34111-3_47. hal-05449791

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# Tracking-by-Self Detection: A
Self-supervised Framework for Multiple
Animal Tracking

C. B. Dev Narayan<fnref n="1" />, Fayaz Rahman<fnref n="1" />, Mohib Ullah<fnref n="2" />✉, Faouzi Alaya Cheikh<fnref n="2" />, Ali Shariq Imran<fnref n="2" />, Christopher Coello<fnref n="3" />, Øyvind Nordbø<fnref n="3" />, G. Santhosh Kumar<fnref n="1" />, and Madhu S. Nair<fnref n="1" />

**Abstract.** Animal tracking is a crucial aspect of animal phenotyping, and industries are using computer vision-based methods to enhance their products. In this paper, we adopt the tracking-by-detection approach and propose a self-supervised framework for multiple animal tracking. Self-supervised learning techniques have recently been employed to train models using unlabeled data and have demonstrated improved accuracy on benchmark datasets. Our proposed framework utilizes an Efficient-Det detector that was pre-trained with self-supervised learning using a modified Barlow twins method. The detected animals are associated with tracks using our proposed variant of Deepsort, which utilizes appearance information to improve the detection-to-track association. We trained and tested the framework on a customized dataset from a Norwegian pig farm, which consisted of four test and four train sequences, as well as a detection dataset containing 1674 labelled frames and 3000 unlabeled images for self-supervised learning. To evaluate the performance of our framework, we used standard tracking metrics such as HOTA (Higher order tracking accuracy), MOTA (Multiple object tracking accuracy), and IDF1 (Identification metrics). The implementation of our framework is publicly available at https://github.com/DeVcB13d/Animal_tracking_with_ssl.

**Keywords:** Tracking · self-supervised detection · self-supervised learning · deepsort · convolutional neural networks

## 1 Introduction

The ability to track animals is crucial for both animal science research and breeding programs. Breeding companies can utilize computer vision-based solutions to monitor animals and develop new traits that can improve breeding programs while also adhering to animal welfare regulations [1]. The study of live animal

<footnote n="1">¹ Artificial Intelligence and Computer Vision Lab, Department of Computer Science, Cochin University of Science and Technology, Kochi 682022, India</footnote>

<footnote n="2">² Norwegian University of Science and Technology, 2815 Gjøvik, Norway
mohib.ullah@ntnu.no</footnote>

<footnote n="3">³ Norsvin SA, Storhamargata 44, 2317 Hamar, Norway</footnote>
behaviour has become an important research field where animals' movements (motion), posture, gestures, facial expressions, and other factors are observed and analyzed [2,3]. Animal behaviour research has its roots in biology, physiology, and animal welfare, all of which can benefit from computer vision algorithms [4]. For example, through animal tracking, we can obtain valuable information that can be used to improve breeding programs [5].

## 2 Related Work

With advances in technology, animal tracking has become critical in animal husbandry. In addition to high-resolution video frames, on-animal sensors like ear tags can also be used to identify and monitor animals [6]. Compared to manual monitoring, computer vision provides a non-invasive and sustainable solution for tracking animals [7]. Recent developments in object detection [8] and segmentation [9] have made tracking-by-detection the leading approach in multiple objects tracking [10–14]. Object detectors are used to identify objects in a frame, and then these detections are associated with a track, contributing to improved tracking accuracy [15]. Self-supervised [16] and Semi-supervised [17] frameworks, which leverage unlabeled data for better model generalization and performance, have recently gained attention over fully supervised learning. Many object detectors use semi-supervised learning to improve their accuracy, such as [18–20]. We developed a self-supervised framework for animal tracking inspired by self-learning. Our framework uses a modified version of Barlow twins [21] to pre-train the EfficientDet-D0 detector on unlabeled data for improved detection results. The detections are then associated with tracks using the Deepsort framework [22].

In a nutshell, the key contributions of our work are:

- We propose a self-supervised learning framework for multiple animal tracking, with the help of an object detector trained by application of a modified Barlow twins method for improvement in multiple object tracking.

- We manually annotated a total of 8 videos for testing and training our framework.

The rest of the paper is organized in the following order. We describe the proposed model in Sect. 3. The dataset details, performance metrics and implementation details are given in Sect. 4. Section 5 lists the quantitative and qualitative results. The discussion and final remarks are given in Sect. 6 that concludes the paper.

## 3 Methodology

### 3.1 Self-supervised Object Detector

In this framework, we use EfficientDet-D0 for the detection of animals in a video frame [23,24]. The detector network comprises an Image-Net pre-trained EfficientNet-B0 as its backbone network [25]. The network uses a feature pyramid network, namely, BiFPN [23], to extract features, which is fed into a class
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Self-detection training architecture: Pre-training with our modified Barlow Twins method is depicted at the top. The resulting detector model, then trained with labelled data, is depicted at the bottom. The weights from the BiFPN and Efficient-Net backbone after the self-supervised learning task are used for the supervised learning task.</figcaption></figure>

and box prediction network for predicting boxes and their corresponding confidence scores. The final boxes are filtered by application of Soft-NMS (Soft - Non-maximum Suppression) [26]. Here, contrary to direct training using labelled data as in a fully supervised learning paradigm, we pre-train the object detection model on unlabelled data using a modified Barlow twins strategy [21] where we apply the method on a condensed representation of the output of the Feature Pyramid Network instead of the detector's backbone. This method aims to learn embeddings invariant to distortions applied to the sample. In other words, it aims to find a representation that conserves information about the sample while being least informative about the specific distortions applied on the frames. The proposed self-detection-based training architecture is depicted in Fig. 1. For pre-training, the representations from the BiFPN(bi-directional feature pyramid network) are each passed through an average pooling layer and the layers are summed and passed through 2 linear layers to obtain the final embeddings, which are used to compute a cross-correlation matrix. During pre-training using unlabelled data, a distorted version of the input image is passed through the network to produce an embedding for each distorted image. The weights learned in the pre-training phase are then transferred for the task of supervised learning, shown at the bottom.

**Loss Function.** The loss function used in pre-training calculates the cross-correlation matrix between the embeddings from the network for each of the different distortions, given by (Eq. 1)

$$L_{bt} = \sum_{i} (1 - C_{ii})^2 + \lambda \sum_{i} \sum_{j \neq i} C_{ij}^2 \quad (1)$$
Here, $\lambda$ is a constant used to trade off the importance of the two loss terms, and $C$ is the cross-correlation matrix with its dimensions the same as that of the output of the projection layer. The cross-correlation matrix between embeddings $A$ and $B$, where $b$ indexes samples and $i, j$ indexes the embeddings given by $z$ is given by (Eq. 2)

$$C_{ij} = \frac{\sum_b z_{b,i}^A z_{b,j}^B}{\sqrt{\sum_b (z_{b,i}^A)^2} \sqrt{\sum_b (z_{b,j}^B)^2}} \quad (2)$$

## 3.2 Modelling of Tracks

The detections from two consecutive frames are modelled as a motion model using the Bayesian filter [27], which is used to predict the new location of the bounding box in the next arriving frame and associate them to a track. Each track ($\mathbf{x}$) is described by an 8-D state space model, which represents the current position, orientation, size, and velocities (or changes in these parameters) of the bounding box over time (Eq. 3).

$$\mathbf{x} = [u, v, \gamma, h, \dot{u}, \dot{v}, \dot{\gamma}, \dot{h}] \quad (3)$$

where $(u, v)$ represent the center of bounding box, $\gamma$ the aspect ratio, and $h$ the height respectively. And $(\dot{u}, \dot{v}, \dot{\gamma}, \dot{h})$ represent their respective velocities in terms of image coordinates. When detection is associated with a target track, the detected bounding box is used to update its target state where the velocity components are solved optimally via the Bayesian framework [27]. If a box is not detected, its new coordinates are updated simply by means of a linear velocity model [10].

## 3.3 Deep Appearance Descriptor

We use a convolutional neural network (CNN) trained on a re-identification (RID) dataset to be able to distinguish between individual animals. The CNN architecture is based on [22]. It is a wide residual network with two convolution layers followed by six residual blocks. The global feature map of dimensionality 128 is computed in a dense layer, followed by final batch normalization and $l_2$ normalization. The appearance descriptor is obtained by passing the area cropped under each detected bounding box through the above CNN to obtain their embeddings which are used as appearance descriptors ($r_j$). The appearance descriptors are used to measure the smallest cosine distance between the descriptors of the jth detection and ith track stored in an array $R$ as shown in (Eq. 4)

$$d^{(2)}(i, j) = \min\{1 - r_j^T r_k^{(i)} | r_k^{(i)} \in R_{(i)}\} \quad (4)$$

## 3.4 Association of Detections

For associating the state predictions in (Eq. 3) from the Bayesian filter described in Sect. 3.2 with the new detections from the detector, a cost matrix $C$ (Eq. 6) is created between the state predictions and detections as a weighted sum of Mahalanobis distance of the boxes (Eq. 5) and cosine distance of the object appearance
descriptors (Eq. 4) which is obtained by passing the cropped part of the frame under the detected bounding box through the CNN described in Sect. 3.3. The squared Mahalanobis distance is given by (Eq. 5)

$$d^{(1)}(i, j) = (d_j - y_i)^T S_i^{-1} (d_j - y_i) \qquad (5)$$

Here, the projection of the track of the i-th object into the measurement space is denoted as $(y_i, S_i)$ and the j-th bounding box detection is denoted as $d_j$. The cost matrix $C$ is calculated as a weighted sum of the squared Mahalanobis distance and the cosine distance shown in (Eq. 6).

$$C_{(i,j)} = \lambda d^{(1)}(i, j) + (1 - \lambda) d^{(2)}(i, j) \qquad (6)$$

Here $\lambda$ is a hyperparameter to trade off the importance between both measurements. The cost matrix is used to solve a linear assignment problem to assign tracks to the newly arrived detections. The overall architecture of our proposed model for tracking is illustrated in Fig. 2.

<figure><img src="image_5.png" /><figcaption>Fig. 2. Tracking architecture: The proposed self-supervised tracking architecture.</figcaption></figure>

# 4 Experiments

## 4.1 Dataset Description

**MOT Dataset.** The data was collected at Norsvin SA's testing facility in Hamar. The animals were housed in groups of eleven or twelve animals of similar sizes in each pen during the growth period from 30-120 kgs. Videos were recorded 24/7 using LOREX (4K Ultra HD IP NVR) and ELOTEC (4MP Bullet, IP67) cameras with a resolution of 1920 × 1080 under different lighting conditions. A selection of 8 different video sequences was split into 4 test and four train video samples (50-50 split). The split was done in accordance with the standard MOT20 benchmark [28]. The videos were manually annotated using CVAT [29], and a sample annotation is shown in Fig. 3. Information on the videos used for tracking is provided in
Table 1. The tracking annotation strictly followed the MOT16 and MOT20 [28, 30] format, with each annotation file being a CSV file containing details of a single bounding box per line, following the specified format given by 7.

$$ (Frame, ID, x, y, w, h, Confidence, Type, Visibility) \qquad (7) $$

where *x* and *y* are the top-left coordinates of the bounding box, *w* and *h* are the height and width of the bounding box, *ID* is an unique number given to the object, *confidence* is the confidence score of the detected box, *Type* is the object class, and *Visibility* its visibility of the object respectively.

<table><caption>**Table 1.** Details of test and train sequences. Each of the videos has a resolution of 1920 × 1080 and a frame rate of 20 FPS</caption><thead><tr><th colspan="3">Training set</th><th colspan="3">Test set</th></tr><tr><th>Video</th><th>Frames</th><th>Animals</th><th>Video</th><th>Frames</th><th>Animals</th></tr></thead><tbody><tr><td>02</td><td>3269</td><td>11</td><td>01</td><td>1994</td><td>11</td></tr><tr><td>04</td><td>590</td><td>11</td><td>03</td><td>1919</td><td>10</td></tr><tr><td>05</td><td>4426</td><td>11</td><td>06</td><td>4429</td><td>11</td></tr><tr><td>07</td><td>4464</td><td>9</td><td>08</td><td>4436</td><td>11</td></tr></tbody></table>

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Sample tracking annotation</figcaption></figure>

## 4.2 Detection Dataset

For the creation of a detection dataset, frames were extracted from a set of videos, different from those used in the MOT dataset, and manually annotated by the Darwin V7 labs [31], and COCO annotator [32]. These annotations contain bounding boxes, segmentation masks, and key points for each animal instance in the
frame. The dataset used in this work contains 1674 images as training data with their ground truths. The ground truths are bounding boxes stored in JSON files created through manual animal boundary tracing by experts using COCO API. The dataset was split into 1339 training data points and 335 testing data points in an 80–20 split.

## 4.3 Evaluation Metrics

We evaluate our model using the metrics: Multiple Object Tracking Accuracy (MOTA), Identification Metrics (IDF1), and Higher Order Tracking Accuracy (HOTA).

**ID Switches and Track Fragmentation:** Identity Switches (IDSW) occurs when two different objects gets their identities exchanged due to close contact or occlusion. Track fragmentation (FRAG) is the number of times an object trajectory gets fragmented or interrupted during tracking.

**Multiple Object Tracking Accuracy:** In MOTA matching is done at a detection level between gtDets (Ground Truth Detection) and prDets (Prediction Detections) [33]. The detections are matched if the bounding boxes have an IoU threshold of $\alpha > 0.5$. Matched detections are considered True Positives (TP) and unmatched False Positives (FP).

$$MOTA = 1 - \frac{|FN| + |FP| + |IDSW|}{|gtDet|} \qquad (8)$$

**Identification Metrics:** The identification metrics does mapping across trajectories in comparison to MOTA that does mapping across detections. IDTPs (identity true positives) are matches over overlapping trajectories, IDFPs (identity false positives) are the remaining gtDets and prDets that are not matched. [34]

$$IDF1 = \frac{|IDTP|}{|IDTP| + 0.5|IDFN| + 0.5|IDFP|} \qquad (9)$$

**Higher Order Tracking Accuracy:** In HOTA, TP, FN and FP are determined at a detection level [34]. To measure associations the metrics TPA(c) (True Positive Association of a TP 'c') which is the set of TPs that have the same gtID and prID as c. The $HOTA_{\alpha}$ for an association-IoU threshold of $\alpha$ is given by (Eq. 10)

$$HOTA_{\alpha} = \sqrt{\frac{\sum_{c \in \{TP\}} A(c)}{|TP| + |FN| + |FP|}} \qquad (10)$$

where, the Jaccard index $A(c)$ is given by (Eq. 11)

$$A(c) = \frac{|TPA(c)|}{|TPA(c)| + |FNA(c)| + |FPA(c)|} \qquad (11)$$
## 4.4 Implementation Details

We conducted all of our experiments using the open-source library PyTorch [35] and OpenCV for processing the videos [36]. The experiments were performed on a machine with 16GB NVIDIA GeForce RTX3080 Laptop GPU. As a pre-processing step, the frames are resized to 500 × 500 and normalized to the range [0, 1] using the mean and standard deviation of Imagenet [37], with mean of [0.485, 0.456, 0.406] and standard deviation of [0.229, 0.224, 0.225].

## 4.5 Training of Self-supervised Object Detector

For pre-training using Barlow Twins, we used the augmentations in Bootstrap Your Own Latent(BYOL) approach [38]. Further implementation details of training of the object detector is given in Table 2. The hyperparameters used were adapted from existing literature.

<table><caption>**Table 2.** Implementation details of pre-training and training of object detector</caption><thead><tr><td>Hyperparameter</td><td>Pre-Training</td><td>Training</td></tr></thead><tbody><tr><td>Batch size</td><td>16</td><td>24</td></tr><tr><td>Learning rate</td><td>1e-4</td><td>3e-3</td></tr><tr><td>Epochs</td><td>50</td><td>24</td></tr><tr><td>Optimizer</td><td>AdamW</td><td>AdamW</td></tr></tbody></table>

## 4.6 Training of Deep Association Model

A re-identification(Re-ID) dataset was constructed from the annotated training videos consisting of 43 unique animals each with 4257 train and 1032 test images. The hyperparameters used are given in Table 3. The hyperparameters used were adapted from existing literature. The deep association model was able to achieve a final classification accuracy of 92%.

<table><caption>**Table 3.** Implementation details of training the deep association model</caption><thead><tr><td>Hyperparameter</td><td>Value</td></tr></thead><tbody><tr><td>Batch size</td><td>64</td></tr><tr><td>Learning rate</td><td>0.1</td></tr><tr><td>Epochs</td><td>54</td></tr><tr><td>Optimizer</td><td>SGD</td></tr></tbody></table>
# 5 Results

The evaluation results of our detection model, is given in Table 4. We evaluate the tracking model on our dataset (Table 1) and the results for each videos are given in Table 5. We compared our framework with popular methods such as Simple Online and Realtime Tracking (SORT) [12] and ByteTrack [14] of which the results are depicted in Table 6. The qualitative results of our model is given in Fig. 4. It can be seen from the comparatively higher values of MOTA and HOTA that the method is able to improve the matching of detections and the value of IDF1 indicates the improved mapping of the object tracks. Also, the method is able to reduce ID switches and track fragmentation. From the results, it is apparent that the proposed method gives improved results proving that our model is on par with the reference and common architectures, whilst having fewer parameters making it suitable for online tracking applications.

<table><caption>**Table 4.** Detection results on validation split of our detection dataset.</caption><thead><tr><td>IoU Threshold</td><td>Area</td><td>mAP</td></tr></thead><tbody><tr><td>0.5 : 0.95</td><td>all</td><td>0.599</td></tr><tr><td>0.5</td><td>all</td><td>0.952</td></tr><tr><td>0.75</td><td>all</td><td>0.707</td></tr></tbody></table>

<table><caption>**Table 5.** Quantitative results of our proposed model on the test set of the tracking dataset.</caption><thead><tr><td>VIDEO</td><td>HOTA(%)</td><td>MOTA(%)</td><td>IDF1(%)</td><td>IDSW</td><td>FRAG</td></tr></thead><tbody><tr><td>02</td><td>62.40</td><td>82.48</td><td>80.90</td><td>73</td><td>221</td></tr><tr><td>04</td><td>62.92</td><td>78.49</td><td>80.51</td><td>16</td><td>22</td></tr><tr><td>05</td><td>62.62</td><td>84.46</td><td>72.42</td><td>50</td><td>90</td></tr><tr><td>07</td><td>78.44</td><td>84.74</td><td>91.30</td><td>26</td><td>2</td></tr><tr><td>Overall</td><td>67.86</td><td>84.74</td><td>80.94</td><td>165</td><td>335</td></tr></tbody></table>

<table><caption>**Table 6.** Comparison of our proposed framework with other tracking methods.</caption><thead><tr><td>Method</td><td>HOTA(%)</td><td>MOTA(%)</td><td>IDF1(%)</td><td>IDSW</td><td>FRAG</td></tr></thead><tbody><tr><td>SORT [12]</td><td>30.71</td><td>75.7</td><td>53.40</td><td>11684</td><td>216</td></tr><tr><td>DEEPSORT [22]</td><td>64.15</td><td>84.17</td><td>74.04</td><td>491</td><td>431</td></tr><tr><td>BYTETRACK [14]</td><td>50.27</td><td>36.89</td><td>54.41</td><td>71</td><td>270</td></tr><tr><td>Proposed</td><td>67.86</td><td>84.74</td><td>80.94</td><td>165</td><td>335</td></tr></tbody></table>
<figure><img src="image_7.png" /><figcaption>Fig. 4. Qualitative results of tracking of multiple animals across frames</figcaption></figure>

## 5.1 Discussion

The potential reasons for the high accuracy and efficiency can be summarized in the following:

- The object detector, which has been trained using self-learning techniques, demonstrates high efficiency and accuracy in detecting objects.

- The framework employs an object detector with 3.8 million compact parameters and a deep appearance descriptor with 2.8 million parameters, enabling efficient online tracking capabilities.

- Leveraging the ReID (Re-Identification) dataset for training the deep appearance descriptor results in improved accuracy in associating objects in our framework.

## 6 Conclusion

We propose a framework for tracking by means of self-learning, wherein we pre-train base object detectors with a modified Barlow Twins by using unlabeled data. A custom-built dataset is used to train and evaluate the model. We are able to achieve improved tracking results with our proposed framework while being highly efficient in terms of the number of parameters.

**Acknowledgment.** We would like to thank Norsvin SA for sharing data and the Research Council of Norway for funding this study, within the BIONÆR program, project numbers 282252 and 321409. In special, we would also like to thank Rune Sagevik, Norsvin SA for the image acquisition.

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