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Andrés Baamonde Lozano
Andrés Baamonde Lozano

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Building an object tracker(I): Building/testing a tracker

#computervision #tracking #opencv

This post series will show a little 'how to'. For building an object tracker with opencv.

Setup environment

The setup it's quite simple, only create a virtual environment and install opencv.

python3 -m venv venv source venv/bin/activate pip install opencv-contrib-python pip install opencv-python
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Our 'Detected' object

I have created a base object for any kind of detection, with common attributes. Positions, detection rectangles and a unique identifier.

class BaseObject(object): identifier = None position = None positions = [] rectangle = None modified = None detections = []
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The tracker

The tracker has a 'track' method that manages new detections, associating it with a previous detection if it's necessary.

That association will be delegated to the detected objects 'equals ' magic method(implemented on the following post)

 class ObjectTracker(object): def __init__(self): self.objects = [] self._current_id = 1 def track(self, obj): match = self.get(obj) if match is None: return self.insert(obj) else: return self.update(obj) def get(self, obj): matches = list( filter( lambda x: x == obj, self.objects)) return matches[0] if len(matches) > 0 else None def insert(self, obj): obj.identifier = self._current_id obj.modified = datetime.now() obj.detections.append(obj.modified) obj.positions = [obj.position] self.objects.append(obj) self._current_id += 1 return obj def update(self, obj): entity = self.get(obj) self.objects = list( filter( lambda x: x.identifier != entity.identifier, self.objects)) entity.position = obj.position entity.positions.append(obj.position) entity.modified = datetime.now() entity.detections.append(entity.modified) self.objects.append(entity) return entity
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Testing it!

Previously to the test we create a object which we initialize with attributes associated to our detection (car attributes, person attributes, some keypoints detected with SIFT/SURF ...) any value that you consider relevant or a unique feature of your objects.

My custom object 

class MycustomObjectClass(BaseObject): my_unique_field = None def __repr__(self): return "{0} {1}".format(self.identifier, self.my_unique_field) def __eq__(self, other): return self.my_unique_field == other.my_unique_field
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Tests

Now we test our tracker class, insert and update methods will the ones that need to be tested.

 class ObjectTrackerTest(unittest.TestCase): def setUp(self): self.tracker = ObjectTracker() def tearDown(self): pass @classmethod def setUpClass(cls): pass @classmethod def tearDownClass(cls): pass def test_insert(self): obj = MycustomObjectClass() obj.my_unique_field = "secret" obj.position = 1 self.tracker.track(obj) self.assertEquals(len(self.tracker.objects), 1) def test_update(self): obj = MycustomObjectClass() obj.my_unique_field = "secret" obj.position = 1 obj2 = MycustomObjectClass() obj2.my_unique_field = "asdf" obj2.position = 2 obj3 = MycustomObjectClass() obj3.my_unique_field = "secret" obj3.position = 3 self.tracker.track(obj) self.tracker.track(obj2) self.assertEquals(len(self.tracker.objects), 2) self.tracker.track(obj3) self.assertEquals(len(self.tracker.objects), 2)
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The features

In the opencv tutorials, there are good examples of feature extration (also feature matching) that can be used for your toy feature extractor.

Delimiting our ROI

But if you are playing with a video. You will prefer delimiting rois(region of interest) because proccesing all image will be expensive, one technique you can apply is background substraction; in this example, you can see a result of a bg substract to give you an idea of the output of the function.

import cv2 video_path = "video/video.avi" cap = cv2.VideoCapture(video_path) fgbg = cv2.createBackgroundSubtractorMOG2() while(1): ret, frame = cap.read() fgmask = fgbg.apply(frame) cv2.imshow('frame-mask', fgmask) cv2.imshow('frame', frame) k = cv2.waitKey(30) & 0xff if k == 27: break cap.release() cv2.destroyAllWindows()
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background subtract

The result of that function is simple, will be a mask of the movement based on the previous state of the screen. Changes on the input frame will be 'marked' . So, you can use the result of that function for masking your current frame.

On the next episode ...

We will detect the object and extract a few of features from it. With the object already detected we put it into the tracker and track it along the video.

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