ICIP is the premier international forum for the technological advances and research results in the fields of theoretical, experimental, and applied image and video processing. The series is sponsored by the IEEE Signal Processing Society and has been held annually since 1994. Research frontiers in fields ranging from traditional image processing applications to evolving multimedia and video technologies are regularly advanced by results first reported in ICIP technical sessions. Topics include, but are not limited to: Procedure 1. Send notice of intent to bid to the Vice President – Conferences and SPS Conference Services staff at sps-conf-proposals@ieee.org. Include in the notice your contact information and the proposed dates and location. 2. Conference Services staff will issue the proposal submission forms upon receipt of the letter of intent. 3. The forms must be completed and the proposal submitted to the Conference Services staff at least three months prior to the next ICIP meeting. See below for more information on the Proposal Timeline. 4. The receipt of the proposal may prompt an invitation to present from the IVMSP Technical Committee Chair to the Committee. 5. The IVMSP Technical Committee will review the proposal, and if it so chooses, will endorse the proposal and forward it to the Conference Board. 6. The Vice President – Conferences may issue an invitation to present to the Conference Board. 7. The Conference Board, if it so chooses, will endorse the proposal and forward it to the Board of Governors for final approval.

IEEE International Conference on Image Processing (ICIP)

Multi-person tracking is often solved with a tracking-by-detection approach that matches all tracks and detections simultaneously based on a distance matrix. In crowded scenes, ambiguous situations with similar track-detection distances occur, which leads to wrong assignments. To mitigate this problem, we propose a new association method that separately treats such difficult situations by modelling ambiguous assignments based on the differences in the distance matrix. Depending on the numbers of tracks and detections, for which the assignment task is determined ambiguous, different strategies to resolve these ambiguous situations are proposed. To further enhance the performance of our tracking framework, we introduce a camera motion-aware interpolation technique and make an adaptation to the motion model, which improves identity preservation. The effectiveness of our approach is demonstrated through extensive ablative experiments with different detection models. Moreover, the superiority w.r.t. other trackers is shown on the challenging MOT17 and MOT20 datasets, where state-of-the-art results are obtained. 

Modelling Ambiguous Assignments for Multi-Person Tracking in Crowds

At present, the main research direction of multi-object tracking framework is detection-based tracking method. Although the detection-based tracking model can achieve good results, it is very dependent on the performance of the detector. The tracking results will be affected to a certain extent when the detector has the behaviors of omission and error detection. Therefore, in order to solve the problem of missing detection, this paper designs a compensation tracker based on Kalman filter and forecast correction. Experiments show that after using the compensation tracker designed in this paper, evaluation indicators have improved in varying degrees on MOT Challenge data sets. In particular, the multi-object tracking accuracy reached 66% in the 2020 datasets of dense scenarios. This shows that the proposed method can effectively improve the tracking performance of the model.

Compensation Tracker: Data Association Method for Lost Object.

At present, the main research direction of multi-object tracking framework is tracking by detection. Although the detection-based tracking framework can achieve good results, it is very dependent on the performance of the detector. The tracking results will be affected to a certain extent when the detector has the behaviors of omission and error detection. Therefore, in order to solve the problem of missing detection, we designs a compensation tracker based on motion compensation and objects selection. Besides the tracker can be embedded into other non-end-to-end tracking frameworks. Experiments show that after using the compensation tracker designed in this paper, evaluation indicators have improved in varying degrees on MOT Challenge datasets. With limit cost, the compensation tracker haves reached 66% MOTA and 67% IDF1 in the 2020 datasets of dense scenarios. This shows that the proposed method can effectively improve the tracking performance of the model.

Compensation Tracker: Reprocessing for Lost Object

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Compensation Tracker: Data Association Method for Lost Object.

Compensation Tracker: Reprocessing for Lost Object