Depth-Aware Image and Video Orientation Estimation
Authors: Muhammad Z. Alam, Larry Stetsiuk, M. Umair Mukati, Zeeshan Kaleem
Organizations: Faculty of Computer science, University of New-Brunswick · Department of Computer Science, Brandon University · Department of Electrical and Photonics Engineering, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark · Department of Electrical and Computer Engineering, COMSATS University
This paper introduces a novel approach for image and video orientation estimation by leveraging depth distribution in natural images. The proposed method estimates the orientation based on the depth distribution across different quadrants of the image, providing a robust framework for orientation estimation suited for applications such as virtual reality (VR), augmented reality (AR), autonomous navigation, and interactive surveillance systems. To further enhance fine-scale perceptual alignment, we incorporate depth gradient consistency (DGC) and horizontal symmetry analysis (HSA), enabling precise orientation correction. This hybrid strategy effectively exploits depth cues to support spatial coherence and perceptual stability in immersive visual content. Qualitative and quantitative evaluations demonstrate the robustness and accuracy of the proposed approach, outperforming existing techniques across diverse scenarios.
Figures & tables
Fig. 1 : Illustration of the orientation estimation pipeline.
Fig. 2 : Illustration of linear perspective in image acquisition.
Fig. 3 : Different viewing angles and their effect on depth displacement in corresponding disparity (inverse of depth) maps.
Method
Sun397 40 % test Accuracy
Sun397 20% test Accuracy
Vailaya et. al. [ 34 ]
80.1 %
–
Tolstaya [ 33 ]
85.6%
–
Ciocca et. al [ 12 ]
89.3%
–
Appia and Narasimah [ 8 ]
54.3%
–
Ciocca et. al [ 13 ]
92.4%
–
Joshi et. al [ 18 ]
–
98.5%
TABLE I : Performance comparison of the proposed method with existing image orientation estimation methods on two different test set splits on Sun397 [ 37 ] dataset adopted in various state-of-the-art methods.
Fig. 4 : Predictions of the proposed method on the subset of images used to produce the results in Table I . Orientation predictions by the proposed method using two different depth estimation techniques. (Depth-map) Image orientations predicted by the proposed method using complete depth-maps. (Dfd) Image orientations predicted by the proposed method using depth from defous estimates.
Fig. 5 : Comparison of the Proposed method with state-of-the-art OAD [ 22 ] on fine-scale (10°increment) image orientation estimation. Delta represents the acceptable error margin in calculating the accuracy.
Fig. 6 : A subset of images selected from four different categories to produce results in Fig. 5
Fig. 7 : Visualization of the image rotations tested for fine-scale image orientation to produce results in Fig. 5
Fig. 8 : Thumbnails of the selected subset of video sequences tested for the evaluation of the proposed orientation estimation technique.
Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China. · Visual Intelligence +X International Cooperation Joint Laboratory of MOE, Beijing 100044, China.