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Custom Object Detector

Project Objectives

  • Built a custom object detector based on caltech101 dataset.
  • Define a JSON file to store all framework configurations.
  • Trained a HOG with SVM model to detect the specific object.
  • Implemented non-maxima suppression to find the best location of object.
  • Applied hard-negative mining techniques to increase the accuracy of object detector.

Software/Packages Used

Algorithms & Methods Used

  • Experiment preparation
    • Define a .json file to store framework configurations.
    • Compute the average object dimensions.
  • Feature extraction
    • Use the average object dimensions to choose appropriate Histogram of Oriented Gradient descriptor dimensions along with sliding window size.
      • Histogram of Oriented Gradient (HOG) descriptor
    • Implement data augmentation to increase dataset size.
    • Extract HOG features from positive and negative image example training images from datasets.
  • Detector training
    • Utilize extracted HOG feature vectors and associated class labels to train a Linear SVM for object detection.
      • Support vector machine
  • Non-maxima suppression
    • Implement non-maxima suppression to reduce overlapping bounding boxes.
  • Hard negative mining
    • Implement hard-negative mining in object detection framework to reduce false positive cases.
  • Detector retrained
    • Use hard-negative mining examples mined from previous step to re-train our object detector.

Approaches

  • The dataset is obatined from Caltech101 and 13 Natural Scene Categories
  • In this project, car side category inside caltech101 dataset is used for building the object detector. Generally speaking, any categories can be used to build the specific object detector. All we need to do is define a .json file to store framework configurations, which looks similar to car.json (check file).

Results

Experiment preparation

Generally speaking, the object detector can be used for detecting any objects (at least, any object categories inside caltech101 dataset), though the car side category is used for demo in this project. For different objects, all we need to do is defining a new (or modifying) the .json file, e.g., in this project, cars.json (check file).

After running explore_dims.py (check file), average object dimensions are computed. The results are shown in Figure 1.

Figure 1: The average dimensions for car side category.

The average object dimensions is helpful for choosing appropriate HOG descriptor dimensions along with sliding window size.

Feature extraction

After running extract_features.py (check file), extracted HOG feature vectors are all saved into .hdf5 file which looks like Figure 2.

Figure 2: car_features.hdf5 for storing extracted features from car side category.

Detector training

After running train_model.py (check file) and test_model_no_nms.py (check file), which implement HOG + SVM to build the detector, initial detector model is finished.

Figure 3 & Figure 4 shows two sample test results (without non max suppression).

Figure 3: Test result for sample # 1 (without non-maxima suppression).

Figure 4: Test results for sample # 2 (without non-maxima suppression).

As the results shown, there are multiple bounding boxes on the object. The best bounding box will be chosen in the next part.

Though there are some false positive cases, such as Figure 5 and Figure 6 shown, this problem will be solved when applying hard negative mining.

Figure 5: False positive case of test results for sample # 3 (without non-maxima suppression).

Figure 6: False positive case of test results for sample # 4 (without non-maxima suppression).

Non-maxima suppression

After applying test_model.py (check file), non-maxima suppression is implemented to find the best bounding box for the object detected.

Figure 7 and Figure 8 demonstrate the test results before and after non-maxima suppression applied.

Figure 7: Test result before and after non-maxima suppression for sample # 1.

Figure 8: Test result before and after non-maxima suppression for sample # 2.

False positive cases are still existing, as Figure 9 & Figure 10 shown, they will be eliminated in next part.

Figure 9: False positive case of test result for sample # 3.

Figure 10: False positive case of test result for sample # 4.

Hard negative mining

After applying hard_negative_mine.py, hard-negative samples are added to .hdf5 file to improve the accuracy of object detector, as Figure 11 shown. And .hdf5 file is a little larger than before, as Figure 12 shown.

Figure 11: Training data with hard-negative samples.

Figure 12: car_features.hdf5 for storing extracted features with hard-negative samples from car side category.

Detector retrained

After retrain the model by using train_model.py, the false positive cases are eliminated, as Figure 13 and Figure 14 shown, comparing to the results in Figure 9 and Figure 10.

Figure 13: Test result with hard-negative mining for sample # 3.

Figure 14: Test result with hard-negative mining for sample # 4.