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This project explores the advancement of people detection in video streams, focusing on innovative methodologies that integrate artistic principles and human body proportions for more accurate identification. The comprehensive study includes problem definition, literature review, detection methodologies utilizing skin and face identification, and experimental results highlighting the performance of the proposed system. Future work aims to refine image and video processing techniques, enhancing detection accuracy and handling a range of detection challenges effectively.
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Cairo University Faculty of Engineering Computer Engineering Department People Detection in Video Stream PresentedBy: Engy Foda Supervised By: Dr. Ahmed Darwish Dr. Ihab Talkhan Dr. Salah El Tawil
Contents • Problem Definition • Motivation • Literature Survey • Art Theories • Artistic People Detection System • Experimental Results in Images • Experimental Results in Video • Future work
Motivation • It is needed by many applications; multimedia applications, traffic control, humanoids and robotics, intelligent cars embedded systems, security.
Challenges • Edge detection, color detectors techniques. • It is hard to model as it is non-rigid object.
Literature survey • People detection in still images • People detection in video • Wavelets and Haar Transform • Detection by components • Dynamic detection information • Tracking • 3D modeling • Kalman filter
Art Theories • Vitruvian Man by Ancient Roman architect Vitruvius • Vitruvian Man by Leonardo Da Vinci • Human Body Proportions Standards Theory • Proportions used in our system
Human Body Proportions Standards Theory • The human body is -in average- of 7 heads high. • Shoulder to shoulder width is 3 heads. • Hip to toes height is 4 heads. • Top of the head to the bottom of the chest is 2 heads high. • Wrist to the end of the outstretched fingers of the hand is 1 head in length. • Top to bottom of the buttocks is 1 head in length. • Elbow to the end of outstretched fingers is 2 heads in length.
Artistic People Detection System • Skin Detection • Face Detection • Human Body Detection
Detection Technique DISCARDED DISCARDED • Detect probable skin regions from the image. • Discard skin regions of area <3% of the whole image area. • Template resize and orientation. • Perform cross correlation. • Apply body proportions and mark body components.
Video Detection Technique • Break the video into successive frames . • Apply the whole image detection technique on each frame. • Assemble the detected frames in a new video file showing the detected persons.
Contributions • Human Body detection based on artistic theory. • Selecting the appropriate proportions from the standard theory. • Using the skin detection and face detection as phases for body detection. • Experimental values of cross correlation [0.5, 0.7].
Advantages • Ability to detect partial bodies. • Detect human body by components. • Does not require fixed setup. • Simple Processing.
Limitations The following cases are not resolved by this system: • Covered faces. • Body is in up side down position. • Pygmies. • Faces with sun glasses, beards, hats. (resolved with low accuracy) • Filtering the regions by area experimentally by <3%.
Samples of results in images Whole Body without background • Correct: • Exact 3 parts • Whole body • 2 parts • False • Fail: • Background • Not Detected • Wrong
Future Work • Modifications on image processing part. • Modifications on video processing part.
Modifications on Image Part • Boundary or contour detection for the human body. • More body components, e.g. every arm, every leg. • Neural networks to learn the human body architecture.
Modifications on Video Part • More processing to the dynamic information of the video sequence.
Thank You efoda@ieee.org
