This thesis presents detailed observation of different Convolutional Neural Network
(CNN) architecture which had assisted Computer Vision researchers to achieve state-of-the-art performance on classification, detection, segmentation and much more to
name image analysis challenges. Due to the advent of deep learning, CNN had
been used in almost all the computer vision applications and that is why there is
utter need to understand the miniature details of these feature extractors and find
out their pros and cons of each feature extractor meticulously. In order to perform
our experimentation, we decided to explore an object detection task using a particular
model architecture which maintains a sweet spot between computational cost and
accuracy. The model architecture which we had used is LSTM-Decoder. The
model had been experimented with different CNN feature extractor and found their
pros and cons in variant scenarios. The results which we had obtained on different
datasets elucidates that CNN plays a major role in obtaining higher accuracy and
we had also achieved a comparable state-of-the-art accuracy on Pedestrian Detection
Dataset.
In extension to object detection, we also implemented two different model architectures which find shoulder keypoints. So, One of our idea can be explicated as
follows: using the detected annotation from object detection, a small cropped image
is generated which would be feed into a small cascade network which was trained
for detection of shoulder keypoints. The second strategy is to use the same object detection model and fine tune their weights to predict shoulder keypoints. Currently,
we had generated our results for shoulder keypoint detection. However, this idea
could be extended to full-body pose Estimation by modifying the cascaded network
for pose estimation purpose and this had become an important topic of discussion
for the future work of this thesis.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/38015 |
| Date | 22 August 2018 |
| Creators | Kapoor, Prince |
| Contributors | Laganière, Robert |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
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