Act as a Video Analytics Handson Expert and write Tensorflow$/$Keras code for Q$3$ and Q$4$ questions$-$

Q$3.$ Implement the video processing technique used in any one published journal for video processing with Tensorflow$/$Keras code

Q$3$ hint$/$prompt : Published Journal for Video Processing $-$ "Real$-$Time Human Detection in Computer Vision: A Survey" by Zhenyu Wu and Navarun Gupta, $2013$ Title: "Real$-$Time Human Detection in Computer Vision: A Survey" Authors: Zhenyu Wu and Navarun Gupta Year: $2013$ Video functionality: The paper surveys techniques for real$-$time human detection in video. Significance of Journal: This journal contributes significantly to the field of computer vision, specifically focusing on real$-$time human detection, which is pivotal in various applications like surveillance, tracking, and human$-$computer interaction. Data Acquisition Technique: The authors primarily acquired data through real$-$time video streams captured by surveillance cameras and other visual sensors$.$ Annotated Data: Human bounding boxes, annotations detailing the location and movement of detected humans in the video frames. Volume of Data: The study utilized a substantial volume of video data, emphasizing the real$-$time nature of the analysis. Video Pre$-$processing Methods: Pre$-$processing included background subtraction, motion segmentation, and noise reduction to enhance human detection accuracy. Video Processing Techniques: Various computer vision algorithms such as Haar cascades, Histogram of Oriented Gradients $($HOG$),$ and Convolutional Neural Networks $($CNNs$)$ were used for human detection. Evaluation $/$ Performance Metric Used: Metrics like precision, recall, F$1-$score, and mean Average Precision $($mAP$)$ were employed to evaluate the accuracy and efficiency of the human detection models. Dataset Used: Multiple datasets could have been utilized, such as the INRIA Person Dataset and the PASCAL VOC dataset References: Zhenyu Wu and Navarun Gupta, "Real$-$Time Human Detection in Computer Vision: A Survey", $2013$

Q$4.$ Implement the DL learning algorithm$/$technique for the journal chosen in Q$3$ with Tensorflow$/$Keras code and compare the results. Include the following in the beginning of the Notebook.

One sentence for the task or application to be achieved

Video processing technique used

Performance metric used, including the equations.

Journal URLs for Q$3$ and Q$4$

List all the dependencies necessary for running the code and specify their versions, in the very beginning of the notebook. Compare the results of Q$3$ and Q$4$ according to the chosen performance metric.

Compare the time taken to execute the code.