1b:I[76749,["/_next/static/chunks/bf18fea59be6d5b1.js","/_next/static/chunks/1ba21a8e081e29d1.js","/_next/static/chunks/0383663887945ae7.js","/_next/static/chunks/2060b26370dc6c0a.js","/_next/static/chunks/c89b4435b8c37fcf.js","/_next/static/chunks/8ddf2550f38c2f2a.js","/_next/static/chunks/5bf36002c846d2d8.js"],"default"] 1c:I[3486,["/_next/static/chunks/bf18fea59be6d5b1.js","/_next/static/chunks/1ba21a8e081e29d1.js","/_next/static/chunks/0383663887945ae7.js","/_next/static/chunks/2060b26370dc6c0a.js","/_next/static/chunks/c89b4435b8c37fcf.js","/_next/static/chunks/8ddf2550f38c2f2a.js","/_next/static/chunks/5bf36002c846d2d8.js"],"default"] 1d:I[17707,["/_next/static/chunks/bf18fea59be6d5b1.js","/_next/static/chunks/1ba21a8e081e29d1.js","/_next/static/chunks/0383663887945ae7.js","/_next/static/chunks/2060b26370dc6c0a.js","/_next/static/chunks/c89b4435b8c37fcf.js","/_next/static/chunks/8ddf2550f38c2f2a.js","/_next/static/chunks/5bf36002c846d2d8.js"],"default"] 1e:I[66528,["/_next/static/chunks/bf18fea59be6d5b1.js","/_next/static/chunks/1ba21a8e081e29d1.js","/_next/static/chunks/0383663887945ae7.js","/_next/static/chunks/2060b26370dc6c0a.js","/_next/static/chunks/c89b4435b8c37fcf.js","/_next/static/chunks/8ddf2550f38c2f2a.js","/_next/static/chunks/5bf36002c846d2d8.js"],"default"] 1f:I[63176,["/_next/static/chunks/bf18fea59be6d5b1.js","/_next/static/chunks/1ba21a8e081e29d1.js","/_next/static/chunks/0383663887945ae7.js","/_next/static/chunks/2060b26370dc6c0a.js","/_next/static/chunks/c89b4435b8c37fcf.js","/_next/static/chunks/8ddf2550f38c2f2a.js","/_next/static/chunks/5bf36002c846d2d8.js"],"default"] 20:I[87099,["/_next/static/chunks/bf18fea59be6d5b1.js","/_next/static/chunks/1ba21a8e081e29d1.js","/_next/static/chunks/0383663887945ae7.js","/_next/static/chunks/2060b26370dc6c0a.js","/_next/static/chunks/c89b4435b8c37fcf.js","/_next/static/chunks/8ddf2550f38c2f2a.js","/_next/static/chunks/5bf36002c846d2d8.js"],"default"] 21:I[26200,["/_next/static/chunks/bf18fea59be6d5b1.js","/_next/static/chunks/1ba21a8e081e29d1.js","/_next/static/chunks/0383663887945ae7.js","/_next/static/chunks/2060b26370dc6c0a.js","/_next/static/chunks/c89b4435b8c37fcf.js","/_next/static/chunks/8ddf2550f38c2f2a.js","/_next/static/chunks/5bf36002c846d2d8.js"],"Group"] 22:I[23365,["/_next/static/chunks/bf18fea59be6d5b1.js","/_next/static/chunks/1ba21a8e081e29d1.js","/_next/static/chunks/0383663887945ae7.js","/_next/static/chunks/2060b26370dc6c0a.js","/_next/static/chunks/c89b4435b8c37fcf.js","/_next/static/chunks/8ddf2550f38c2f2a.js","/_next/static/chunks/5bf36002c846d2d8.js"],"default"] 23:T555,In this paper, we propose a robust non-local TV-L¹ optical flow method with occlusion detection to address the problem of weak robustness of optical flow estimation with motion occlusion. First, a TV-L¹ form for flow estimation is defined using a combination of the brightness constancy and gradient constancy assumptions in the data term and by varying the weight under the Charbonnier function in the smoothing term. Second, to handle the potential risk of the outlier in the flow field, a general non-local term is added in the TV-L¹ optical flow model to engender the typical non-local TV-L¹ form. Third, an occlusion detection method based on triangulation is presented to detect the occlusion regions of the sequence. The proposed non-local TV-L¹ optical flow model is performed in a linearizing iterative scheme using improved median filtering and a coarse-to-fine computing strategy. The results of the complex experiment indicate that the proposed method can overcome the significant influence of non-rigid motion, motion occlusion, and large displacement motion. Results of experiments comparing the proposed method and existing state-of-the-art methods by, respectively, using Middlebury and MPI Sintel database test sequences show that the proposed method has higher accuracy and better robustness.

Mingrun Wang

Publications