Vehicle detection and tracking using deep learning¶

🔬 Data Science

🥠 Deep Learning and Object Detection

🛤️ Tracking

Table of Contents¶

Introduction and objective
Necessary imports
Prepare data that will be used for training
Model training
Inference and tracking
Conclusion

Introduction and objective¶

Vehicle detection and tracking is a common problem with multiple use cases. Government authorities and private establishment might want to understand the traffic flowing through a place to better develop its infrastructure for the ease and convenience of everyone. A road widening project, timing the traffic signals and construction of parking spaces are a few examples where analysing the traffic is integral to the project.

Traditionally, identification and tracking has been carried out manually. A person will stand at a point and note the count of the vehicles and their types. Recently, sensors have been put into use, but they only solve the counting problem. Sensors will not be able to detect the type of vehicle.

In this notebook, we'll demonstrate how we can use deep learning to detect vehicles and then track them in a video. We'll use a short video taken from live traffic camera feed.

Necessary imports¶

In [1]:

import pandas as pd

from arcgis.learn import RetinaNet, prepare_data

Prepare data that will be used for training¶

You can download vehicle training data from here. Extract the downloaded file to get your training data.

Model training¶

Let's set a path to the folder that contains training images and their corresponding labels.

In [ ]:

data_path = "data/vehicle_detection"

We'll use the prepare_data function to create a fastai databunch with the necessary parameters such as batch_size, and chip_size. A complete list of parameters can be found in the API reference.

The given dataset has 235 images of size 854x480 pixels. We will define a chip_size of 480 pixels which will create random crops of 480x480 from the given images. This way we will maintain the aspect ratios of the objects but can miss out on objects when training the model for fewer epochs. To avoid cropping, we can set resize_to=480 so that every chip is an entire frame and doesn't miss any object, but there is a risk of poor detection with smaller sized object.

In [3]:

data = prepare_data(data_path, 
                    batch_size=4, 
                    dataset_type="PASCAL_VOC_rectangles", 
                    chip_size=480)

Please check your dataset. 9 images dont have the corresponding label files.

We see the warning above because there are a few images in our dataset with missing corresponding label files. These images will be ignored while loading the data. If it is a significant number, we might want to fix this issue by adding the label files for those images or removing those images.

We can use the classes attribute of the data object to get information about the number of classes.

In [4]:

data.classes

Out[4]:

['background',
 'bicycle',
 'bus',
 'car',
 'motorcycle',
 'person',
 'scooter',
 'tempo',
 'tractor',
 'truck',
 'van']

Visualize training data¶

To visualize and get a sense of the training data, we can use the data.show_batch method.

In [14]:

data.show_batch()

In the previous cell, we see a sample of the dataset. We can observe, in the given chips, that the most common vehicles are cars and bicycles. It can also be noticed that the different instance of the vehicles have varying scales.

Load model architecture¶

arcgis.learn provides us object detection models which are based on pretrained convnets, such as ResNet, that act as the backbones. We will use RetinaNet with the default parameters to create our vehicle detection model. For more details on RetinaNet check out How RetinaNet works? and the API reference.

In [6]:

retinanet = RetinaNet(data)

We will use the lr_find() method to find an optimum learning rate. It is important to set a learning rate at which we can train a model with good accuracy and speed.

In [7]:

retinanet.lr_find()

Out[7]:

4.365158322401661e-05

Train the model¶

We will now train the RetinaNet model using the suggested learning rate from the previous step. We can specify how many epochs we want to train for. Let's train the model for 100 epochs. Also, we can turn tensorboard True if we want to visualize the training process in tensorboard.

In [8]:

retinanet.fit(100, lr=4.365158322401661e-05, tensorboard=True)

epoch	train_loss	valid_loss	time
0	2.651160	3.122699	00:33
1	2.727485	3.089710	00:32
2	2.744920	3.015922	00:32
3	2.671797	2.851994	00:31
4	2.457554	2.497410	00:31
5	2.381740	2.328834	00:31
6	2.060174	4.138567	00:31
7	1.792403	21.451857	00:31
8	1.712977	4.193508	00:31
9	1.608706	4.876813	00:32
10	1.496329	4.955950	00:32
11	1.575526	2.124239	00:33
12	1.448479	2.765982	00:31
13	1.356783	2.739088	00:31
14	1.296036	1.941170	00:32
15	1.235588	3.042969	00:32
16	1.177469	2.916740	00:32
17	1.163151	2.462182	00:32
18	1.124477	1.952319	00:32
19	1.055723	2.639346	00:32
20	0.976554	1.884056	00:32
21	0.865862	1.545389	00:32
22	0.885476	1.693674	00:32
23	0.861983	1.386624	00:32
24	0.812286	1.257245	00:33
25	0.794138	1.578588	00:32
26	0.765640	1.208835	00:34
27	0.702818	1.117395	00:32
28	0.669110	1.213653	00:33
29	0.674798	1.130191	00:32
30	0.675300	1.154881	00:32
31	0.680791	1.257907	00:33
32	0.655586	1.072347	00:32
33	0.586407	1.009210	00:32
34	0.570755	1.220290	00:33
35	0.590223	0.982790	00:34
36	0.575041	0.997690	00:33
37	0.585412	1.035814	00:33
38	0.572887	1.015082	00:33
39	0.552126	0.949728	00:32
40	0.535455	1.195224	00:33
41	0.499169	0.946746	00:33
42	0.527345	1.009812	00:34
43	0.547029	0.991675	00:33
44	0.515441	0.906661	00:33
45	0.547948	0.986166	00:33
46	0.517109	0.943002	00:33
47	0.474826	0.894875	00:33
48	0.440434	0.909886	00:33
49	0.441918	0.819840	00:33
50	0.433040	0.837711	00:33
51	0.424501	0.834161	00:33
52	0.442397	0.825194	00:33
53	0.438501	0.778577	00:34
54	0.425794	0.790809	00:33
55	0.405544	0.774125	00:34
56	0.397529	0.751094	00:34
57	0.386021	0.756899	00:33
58	0.395799	0.763772	00:33
59	0.385372	0.785581	00:35
60	0.379765	0.767338	00:34
61	0.369503	0.720050	00:33
62	0.367806	0.720712	00:35
63	0.378731	0.734859	00:34
64	0.368838	0.729135	00:33
65	0.344555	0.700024	00:35
66	0.340411	0.743908	00:35
67	0.350800	0.718764	00:34
68	0.364890	0.715524	00:35
69	0.337952	0.688673	00:34
70	0.348077	0.719215	00:35
71	0.323196	0.700020	00:34
72	0.361027	0.719423	00:35
73	0.367712	0.719814	00:35
74	0.367507	0.693808	00:35
75	0.347651	0.708264	00:35
76	0.345269	0.705601	00:34
77	0.341163	0.719633	00:34
78	0.321359	0.719021	00:34
79	0.325086	0.710695	00:34
80	0.307621	0.709985	00:34
81	0.312010	0.695209	00:34
82	0.308455	0.723050	00:34
83	0.333749	0.721235	00:34
84	0.323337	0.718696	00:33
85	0.330353	0.709316	00:34
86	0.337785	0.728645	00:33
87	0.299953	0.732279	00:33
88	0.309058	0.723001	00:33
89	0.341413	0.749138	00:33
90	0.332262	0.734328	00:33
91	0.306863	0.716808	00:33
92	0.300803	0.737754	00:33
93	0.313041	0.714918	00:33
94	0.329477	0.711772	00:33
95	0.321354	0.714558	00:33
96	0.321379	0.701373	00:34
97	0.301340	0.726296	00:33
98	0.297174	0.726158	00:33
99	0.310064	0.736690	00:33

After the training is complete, we can view the plot with training and validation losses.

In [21]:

retinanet.learn.recorder.plot_losses()

Visualize results on validation set¶

To see sample results we can use the show_results method. This method displays the chips from the validation dataset with ground truth (left) and predictions (right). We can also specify the threshold to view predictions at different confidence levels. This visual analysis helps in assessing the qualitative results of the trained model.

In [18]:

retinanet.show_results(thresh=0.4)

To see the quantitative results of our model we will use the average_precision_score method.

In [19]:

retinanet.average_precision_score(detect_thresh=0.4)

100.00% [6/6 00:01<00:00]

Out[19]:

{'bicycle': 0.6121794875615674,
 'bus': 0.0,
 'car': 0.770548729309354,
 'motorcycle': 0.0,
 'person': 0.0,
 'scooter': 0.0,
 'tempo': 0.0,
 'tractor': 0.0,
 'truck': 1.0,
 'van': 0.38429487869143486}

We can see the average precision for each class in the validation dataset. Note that while car and bicycle have a good score, van doesn't, and a few have a score of 0. Remember when we visualized the data using show_batch we noted that the cars and bicycles were the most common objects. It means, the scores could be correlated with the number of examples of these objects we have in our training dataset.

Let's look at the number of instances of each class in the training data and it should explain.

In [22]:

all_classes = []
for i, bb in enumerate(data.train_ds.y):
    all_classes += bb.data[1].tolist()
    
df = pd.value_counts(all_classes, sort=False)
df.index = [data.classes[i] for i in df.index] 
df

Out[22]:

bicycle       266
bus            19
car           756
motorcycle     33
person         24
scooter         6
tempo           1
tractor         4
truck          30
van            69
dtype: int64

It is evident that the classes that have a score of 0.0 have extremely low number of examples in the training dataset.

Save the model¶

Let's save the model by giving it a name and calling the save method, so that we can load it later whenever required. The model is saved by default in a directory called models in the data_path initialized earlier, but a custom path can be provided.

In [11]:

retinanet.save('vehicle_det_ep100_defaults')

Out[11]:

WindowsPath('vehicle_detection/models/vehicle_det_ep100_defaults')

Inference and tracking¶

Multiple-object tracking can be performed using predict_video function of the arcgis.learn module. To enable tracking, set the track parameter in the predict_video function as track=True.

The following options/parameters are available in the predict video function for the user to decide:-

vanish_frames: The number of frames the object remains absent from the frame to be considered as vanished.
detect_frames: The number of frames an object remains present in the frame to start tracking.
assignment_iou_thrd: There might be multiple trackers detecting and tracking objects. The Intersection over Union (iou) threshold can be set to assign a tracker with the mentioned threshold value.

In [ ]:

retinanet.predict_video(input_video_path=r'data/test.mp4', 
                        metadata_file=r'data/vid1.csv',
                        track=True, 
                        visualize=True, 
                        threshold=0.5,
                        resize=True)

We can count the number of vehicles per unit of time and update a feature layer with the live count of cars, buses, trucks etc. When this process is done for multiple intersections within the city, an ArcGIS dashboard can be created. It queries the continually updated feature layers and displays the results using a dashboard such the following:

Conclusion¶

In this notebook, we have learnt how to automate multi-object tracking and counting system. This will not only help in intelligent traffic management but can be found useful in wide variety of applications.

ArcGIS API for Python Sample Notebooks

Vehicle detection and tracking using deep learning¶

Table of Contents¶

Introduction and objective¶

Necessary imports¶

Prepare data that will be used for training¶

Model training¶

Visualize training data¶

Load model architecture¶

Train the model¶

Visualize results on validation set¶

Save the model¶

Inference and tracking¶

Conclusion¶