Hybrid Approach for Object Detection from Video Using Deep Learning for Tag Recommendation

The rapid growth of video data in various domains has led to an increased demand for effective and efficient methods to analyze and extract valuable information from videos. The problem of object detection from video in the computer vision domain is a challenging task without many involved applications, including recommender systems, surveillance, autonomous driving, and video analytics. Deep learning techniques have shown remarkable success in object detection, but their performance heavily relies on large-scale labeled datasets. This study proposes a novel model for object detection from video by combining deep learning and transfer learning algorithms. The proposed model takes advantage of the power of employing convolutional neural networks (CNNs) to extract spatio temporal features from the video frames. To address the limited labeled video data, transfer learning is employed, where a pre-trained CNN model, such as ResNet, is fine-tuned on the UCF101, Sports1M and Youtube8M Video datasets. Transfer learning enables the model to learn generalizable features from these rich datasets, enhancing its ability to detect objects in unseen videos. Furthermore, the proposed model incorporates temporal information by employing LSTM or 3D convolutional networks to capture the motion dynamics across consecutive frames. Spatial and temporal features fusion enhance the robustness and accuracy of object detection. Proposed model is used extensively to evaluate on the UCF101, Sports1M and YouTube8M Dataset. The proposed model effectively determines the results that show localizing and classifying objects in video sequences, outperforming existing state-of-the-art methods. Overall, the proposed research provides a promising approach for object detection from video, showcasing the Deep learning and transfer learning algorithms' potential in tackling the challenges of limited labeled video data and exploiting the spatio-temporal context for improved object detection performance.