Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Today's person detection methods work best when people are in common upright poses and appear reasonably ll spaced out in the image. Hover, in many real images, that's not what people do. People often appear quite close to each other, e.g., with limbs linked or heads touching, and their poses are often not pedestrian-like.  propose an approach to detangle people in multi-person images.  formulate the task as a region assembly problem. Starting from a large set of overlapping regions from body part semantic segmentation and generic object proposals, our optimization approach reassembles those pieces together into multiple person instances. It enforces that the composed body part regions of each person instance obey constraints on relative sizes, mutual spatial relationships, foreground coverage, and exclusive label assignments when overlapping. Since optimal region assembly is a challenging combinatorial problem,  present a Lagrangian relaxation method to accelerate the lor bound estimation, thereby enabling a fast branch and bound solution for the global optimum. As output, our method produces a pixel-level map indicating both 1) the body part labels (arm, leg, torso, and head), and 2) which parts belong to which individual person. Our results on three challenging datasets show our method is robust to clutter, occlusion, and complex poses. It outperforms a variety of competing methods, including existing detector CRF methods and region CNN approaches. In addition,  demonstrate its impact on a proxemics recognition task, which demands a precise representation of "whose body part is where" in crowded images.
['Hao Jiang', 'Kristen Grauman']
I am writing a research paper on a idea Detangling People: Individuating Multiple Close People and Their Body Parts via Region Assembly
[{"from": "human", "value": "I am writing a research paper on a idea Detangling People: Individuating Multiple Close People and Their Body Parts via Region Assembly"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Today's person detection methods work best when people are in common upright poses and appear reasonably ll spaced out in the image. Hover, in many real images, that's not what people do. People often appear quite close to each other, e.g., with limbs linked or heads touching, and their poses are often not pedestrian-like.  propose an approach to detangle people in multi-person images.  formulate the task as a region assembly problem. Starting from a large set of overlapping regions from body part semantic segmentation and generic object proposals, our optimization approach reassembles those pieces together into multiple person instances. It enforces that the composed body part regions of each person instance obey constraints on relative sizes, mutual spatial relationships, foreground coverage, and exclusive label assignments when overlapping. Since optimal region assembly is a challenging combinatorial problem,  present a Lagrangian relaxation method to accelerate the lor bound estimation, thereby enabling a fast branch and bound solution for the global optimum. As output, our method produces a pixel-level map indicating both 1) the body part labels (arm, leg, torso, and head), and 2) which parts belong to which individual person. Our results on three challenging datasets show our method is robust to clutter, occlusion, and complex poses. It outperforms a variety of competing methods, including existing detector CRF methods and region CNN approaches. In addition,  demonstrate its impact on a proxemics recognition task, which demands a precise representation of \"whose body part is where\" in crowded images."}]