# A Robust Temporal Alignment Technique for Infrared and Visible-Light Video Sequences

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3 15 IR Visible light 5 Movement 0 5 Fig. 3. Results of the feature point localization. (Left) Zoomed version of the IR image of Fig. 1 showing a single light bulb region with superimposed ellipse, and the obtained feature point before (circle) and after (cross) the minimization of eq. 1. (Right) Final feature point positions for a few selected light bulb regions. available a-priori knowledge of the light bulb locations on the alignment board to improve the precision of the computed feature point locations. For this purpose, let us introduce some concepts of projective geometry. In more detail, let the physical position of a feature point on the alignment board be represented by the homogeneous vector x = [x y 1] T and its counterpart within an arbitrary image of an IR/visible-light image sequence pair as x = [x y 1] T. Now, given a set of feature points x i and a corresponding set of feature points x i, there exists a projective transformation that takes each x i to x i. Thus, our goal is to find a 3 3 homography matrix H such that Hx i = x i for each i. However, due to the fact that the feature point locations obtained in the previous step were measured inexactly, there may not exist an homography which is able to map the feature point positions from the alignment board to the IR/visible-light images. A common solution to this problem is the usage of the Direct Linear Transformation (DLT) algorithm [7] which results in a first approximation of the underlying homography. However, this approximation is not optimal since it is obtained by employing the singular value decomposition which minimizes an algebraic distance measure that is not physically meaningful. Thus, we further refine the resultant homography by using the following cost function x i Hx i 2, (1) i where i = 1,...,81 indexes the positions of the corresponding feature points. The final, refined homography is the one for which the geometric error given by eq. (1) is minimized using e.g. the iterative Levenberg-Marquardt algorithm [7]. The final feature point locations are obtained by applying the refined homography to the feature point positions within the alignment board coordinate system. The left-hand side of Fig. 3 shows the resulting feature point position for a single light bulb region before and after minimizing the cost function of eq. (1). The final feature point positions for a few selected light bulb regions of the IR image of Fig. 1 are depicted in the right-hand side of Fig. 3. It will be shown in the next section that by means of these extracted feature point positions, the time-shift between two unsynchronized IR and visible-light video sequences can be successfully determined Frame Number Fig. 4. Example of the vertical component of the speed of a single feature point along an IR video sequence (solid line) and a visible-light video sequence (dashed line). III. TEMPORAL ALIGNMENT Let S V and S I be two video sequences N V and N I long, recorded at the same frame rate by a visible-light and IR camera, respectively, exhibiting different poses of the alignment board of Fig. 1. Finding the temporal offset ˆt between the two video sequences S V and S I is equivalent to maximizing a similarity measure s( ) over a set of potential temporal offset candidates t such that ˆt = arg max t s ( S V,S I, t ). (2) Please note that in what follows, we start from the premise that the two video cameras are mounted side by side on a rail, hence, we assume that their position only differs horizontally and is identical otherwise. The proposed temporal alignment approach starts off by performing translational movements of the alignment board in the downward and upward direction, respectively. This is followed by the extraction of the feature point positions in each frame of the IR and visible-light video sequence as elaborated in Section II. Based on the extracted feature point positions, we determine the vertical component of the speed of each feature point along the video sequence. This is accomplished by subtracting the y-coordinates of the feature point positions between two successive video frames. Fig. 4 shows an example of the vertical speed of a single feature point along an IR/visible-light video sequence pair. From the depicted curves the downward and upward swing of the alignment board, given by the negative and positive portions of the curves, respectively, can be seen. This is particularly apparent when looking at the dashed line, representing the visible-light video sequence. Please note that the scale difference between the amplitudes of the two curves is due to a mismatch in image resolution between the IR and visible-light video sequences. Another way to look at the problem of temporal alignment is presented in Fig. 5. Here, the global movement of all 81 feature points is displayed as an image, with each line representing the overall vertical movement of a single feature point. In both images, brighter pixel values indicate the displacement of the alignment board in the upward direction whereas darker pixel values suggest a downward movement of the alignment

4 Similarity Fig. 5. Global movement of all 81 feature points within the visible-light (left) and IR video sequence (right). Each line represents the vertical movement of a single feature point. Bright pixel values indicate an upward movement whereas dark pixel values represent a downward movement of the alignment board. board. Based on Fig. 5, the temporal offset we are looking for corresponds to the horizontal displacement between the two images such that the horizontal cross-correlation is maximized. Let us put this observation now in a mathematical context. Given a temporal offset candidate t, the similarity between the visible-light sequence S V and the IR sequence S I is given by s(s V,S I, t) = M M V (m, n t)m I (m, n) m=1 n N m=1 n N, (3) M ( MV (m, n t) ) 2 K ( MI (k, l) ) 2 k=1 l N where the matrices M V (m, n) and M I (m, n) express the movement of the m th feature point between two consecutive visible-light and IR frames at time instant n, respectively, and N = { n 1 (n t) N V 1 n N I }. Please note that the similarity measure of eq. (3) is bounded to the interval [ 1,1]. The two video sequences are considered identical if the similarity measure is 1 and complementary to each other if the result is 1. A result of 0 implies that no similarities between the two sequences could be found. Finally, as expressed in eq. (2), the actual temporal offset ˆt between the IR and visible-light video sequence is the one for which eq. 3 is maximized. Fig. 6 shows the results of the temporal alignment for the two IR/visible-light video sequences corresponding to Fig. 5. It can be observed that the highest similarity (according to eq. (3)) is obtained for a temporal offset of 51 frames. This result corresponds well with Fig. 5 which, when evaluated subjectively, suggests a time-shift of approximately 50 frames between the two sequences. IV. RESULTS In order to show the effectiveness of the proposed framework for the temporal alignment of IR/visible-light video sequence pairs, we performed experiments with five different, manually recorded data sets. Apart from the actual scene content (which may vary from sequence to sequence), each video exhibits different poses of the alignment board of Fig. 1. These poses include translational and rotational movements of the alignment board and were chosen in such a way that, t Fig. 6. Result of the temporal alignment for the two IR and visible-light video sequences corresponding to Fig. 5. The highest similarity (according to eq. (3)) between the two video sequences is obtained for a temporal offset t of 51 frames. TABLE I RESULTS OF THE TEMPORAL OFFSET ESTIMATION FOR FIVE DIFFERENT IR/VISIBLE-LIGHT VIDEO SEQUENCE PAIRS. 1 st 2 nd 3 rd 4 th 5 th pair pair pair pair pair Temporal Offset Similarity both, temporal alignment and a future stereo calibration of the employed cameras can be performed simultaneously. The IR video sequences were obtained by recording the analogue NTSC video output of a FLIR Prism DS camera, operating at a spectral range of 3.6 to 5 µm. In order to convert the analogue video stream to digital video, a Blackmagic Decklink HD Extreme 3D video capturing card was utilized. In accordance with the NTSC standard, the resultant video exhibits a resolution of (which differs from the native resolution of the employed IR camera, ). As for the visible-light video sequences a Panasonic HDC-TM700 camera was employed. The corresponding videos exhibit a resolution of Both, IR and visible-light video sequences were recorded at a rate of 30 frames per second. The estimated temporal offsets ˆt for all five tested video sequences together with the corresponding similarity measures of eq. (3) are given in Table I. Note that the attained similarity is close to one for all five tested scenarios. This implies that after temporal alignment the movements of the alignment board are almost identical between the IR and visible-light video sequences. However, it is worth noting that the overall similarity measure depends on the performed movements with the alignment board. Thus, a lower similarity does not necessarily suggest a poor estimation of the temporal offset. Furthermore, by plotting the similarity measures over the whole set of temporal offset candidates (see Fig. 6) for each tested video pair, we observed that the curves always exhibit a single distinct peak at the position of the correct temporal offset, indicating a high robustness of our framework. Finally, in order to qualitatively demonstrate the effectiveness of our proposed temporal alignment scheme, Fig. 7 shows the superposition of five frames from the 5 th IR/visible-light video sequence pair before and after temporal alignment. It

5 Fig. 7. Five representative frames (58, 88, 154, 223, 296) from the 5 th IR/visible-light video sequence pair before (top row) and after (bottom row) temporal alignment. For visualization purposes, corresponding frames of the two sequences were superimposed and occupy the red (IR images) and green (visible-light images) bands of the depicted RGB pseudo-color images. can be noted that the unsynchronized video frames (top row) display a significant misalignment in time. This is particularly evident when observing the four IR video frames to the right which appear to lag considerably behind the visiblelight frames. As for the synchronized video frames (bottom row), it can be seen that both IR and visible-light frames exhibit similar poses of the alignment board, thus, indicating the correct temporal alignment of the IR/visible-light video sequence pair. V. CONCLUSION In this work, a novel technique for the robust temporal alignment of IR and visible-light video sequences was introduced. Our method utilizes a planar alignment device equipped with miniature light bulbs to increase the number of corresponding feature points within the frames of a misaligned IR/visiblelight video sequence pair. Thereby, the alignment process is turned more robust against the chronic lack of mutual scene characteristics, which represents a common source of problems when synchronizing video sequences originating from different spectral modalities. The proposed processing chain first determines the exact light bulb positions in the individual frames of both IR and visible-light video sequences and computes the overall movement of the feature points along the video sequences. Subsequently, these movements are used to solve for the correct temporal offset. We assessed our framework by performing experiments with five different, misaligned IR/visible-light sequence pairs. These tests suggested that the proposed temporal alignment technique is able to estimate the temporal offset with a very high confidence level. ACKNOWLEDGEMENTS The authors would like to thank the Brazilian Funding Agency CAPES (Pro-Defesa) for the financial support. REFERENCES [1] Y. Caspi and M. Irani, Spatio-temporal alignment of sequences, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 11, pp , [2] J. Yan and M. Pollefeys, Video synchronization via space-time interest point distribution, in Advanced Concepts for Intelligent Vision Systems, 2004, pp [3] Y. Caspi, D. Simakov, and M. Irani, Feature-based sequence-tosequence matching, International Journal of Computer Vision, vol. 68, no. 1, pp , June [4] L. Wolf and A. Zomet, Wide baseline matching between unsynchronized video sequences, International Journal of Computer Vision, vol. 68, no. 1, pp , June [5] C. Lei and Y.-H. Yang, Tri-focal tensor-based multiple video synchronization with subframe optimization, IEEE Transactions on Image Processing, vol. 15, no. 9, pp , [6] F.L.C. Padua, R.L. Carceroni, G.A.M.R. Santos, and K.N. Kutulakos, Linear sequence-to-sequence alignment, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 2, pp , 20. [7] R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, Cambridge University Press, 2nd edition, [8] Y. Ukrainitz and M. Irani, Aligning sequences and actions by maximizing space-time correlations, in Proceedings of the 9th European Conference on Computer Vison, 2006, vol. 3953, pp [9] M. Ushizaki, T. Okatani, and K. Deguchi, Video synchronization based on co-occurrence of appearance changes in video sequences, in Proceedings of the 18th International Conference on Pattern Recognition, 2006, vol. 3, pp [] S. Prakash, P.Y. Lee, T. Caelli, and T. Raupach, Robust thermal camera calibration and 3D mapping of object surface temperatures, in Proceedings of the XXVIII SPIE Conference on Thermosense, 2006, vol [11] M. Gschwandtner, R. Kwitt, A. Uhl, and W. Pree, Infrared camera calibration for dense depth map construction, in Proceedings of the 2011 Intelligent Vehicles Symposium, June 2011, pp [12] A. Ellmauthaler, Pagliari C.L. da Silva, E.A.B., and Gois J.N., A novel iterative calibration approach for thermal infrared cameras, submitted for publication in Proceedings of the 2013 IEEE International Conference on Image Processing. [13] C.D. Prakash and L.J. Karam, Camera calibration using adaptive segmentation and ellipse fitting for localizing control points, in Proceedings of the 2012 IEEE International Conference on Image Processing, October 2012, pp [14] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, Springer Verlag, New York, 2nd edition, [15] J. Canny, A computational approach to edge detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp , November [16] A.W. Fitzgibbon, M. Pilu, and R.B. Fisher, Direct least-squares fitting of ellipses, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 5, pp , May 1999.

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