Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟯𝟰𝟯 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵𝗲𝗿 𝗳𝗿𝗼𝗺 𝗨𝗖 𝗕𝗲𝗿𝗸𝗲𝗹𝗲𝘆 𝗵𝗮𝘀 𝗽𝘂𝗯𝗹𝗶𝘀𝗵𝗲𝗱 𝗧𝗿𝗮𝗰𝗸𝗶𝗻𝗴 𝗣𝗲𝗼𝗽𝗹𝗲 𝗯𝘆 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗻𝗴 𝟯𝗗 𝗔𝗽𝗽𝗲𝗮𝗿𝗮𝗻𝗰𝗲, 𝗟𝗼𝗰𝗮𝘁𝗶𝗼𝗻 & 𝗣𝗼𝘀𝗲 Follow me for a similar post: 🇮🇳 Ashish Patel ------------------------------------------------------------------- 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗙𝗮𝗰𝘁𝘀 : 🔸 Paper: 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗻𝗴 𝟯𝗗 𝗔𝗽𝗽𝗲𝗮𝗿𝗮𝗻𝗰𝗲, 𝗟𝗼𝗰𝗮𝘁𝗶𝗼𝗻 & 𝗣𝗼𝘀𝗲 🔸 This paper is published arxiv2021. 🔸 Presented PHALP, an approach for monocular people tracking, by predicting appearance, location and pose in 3D. Our method relies on a powerful backbone for 3D human mesh recovery, modeling on the tracklet level for collecting information across the tracklet’s detections, and eventually predicting the future states of the tracklet. ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 In this paper, we present an approach for tracking people in monocular videos, by predicting their future 3D representations. To achieve this, we first lift people to 3D from a single frame in a robust way. This lifting includes information about the 3D pose of the person, his or her location in the 3D space, and the 3D appearance. As we track a person, we collect 3D observations over time in a tracklet representation.  🔸 Given the 3D nature of our observations, we build temporal models for each one of the previous attributes. We use these models to predict the future state of the tracklet, including 3D location, 3D appearance, and 3D pose.  🔸 For a future frame, we compute the similarity between the predicted state of a tracklet and the single frame observations in a probabilistic manner. Association is solved with simple Hungarian matching, and the matches are used to update the respective tracklets. We evaluate our approach on various benchmarks and report state-of-the-art results. ------------------------------------------------------------------- #computervision #artificialintelligence #innovation -------------------------------------------------------------------