Kapitel 7 “Tracking” – p. 1 Tracking  Fundamentals  Object representation  Object detection  Object tracking A. Yilmaz, O. Javed, and M. Shah Object tracking: A survey ACM Computing Surveys, Vol. 38, No. 4, 1-45, 2006 TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AA Kapitel 7

Kapitel 7 “Tracking” – p. 2 Fundamentals (1)

Kapitel 7 “Tracking” – p. 3 Fundamentals (2) Applications of object tracking:  motion-based recognition: human identification based on gait, automatic object detection, etc.  automated surveillance: monitoring a scene to detect suspicious activities or unlikely events  video indexing: automatic annotation and retrieval of the videos in multimedia databases  human-computer interaction: gesture recognition, eye gaze tracking for data input to computers, etc.  traffic monitoring: real-time gathering of traffic statistics to direct traffic flow  vehicle navigation: video-based path planning and obstacle avoidance capabilities

Kapitel 7 “Tracking” – p. 4 Fundamentals (3) Tracking task:  In the simplest form, tracking can be defined as the problem of estimating the trajectory of an object in the image plane as it moves around a scene. In other words, a tracker assigns consistent labels to the tracked objects in different frames of a video. Additionally, depending on the tracking domain, a tracker can also provide object- centric information, such as orientation, area, or shape of an object.  Two subtasks: Build some model of what you want to track Use what you know about where the object was in the previous frame(s) to make predictions about the current frame and restrict the search Repeat the two subtasks, possibly updating the model

Kapitel 7 “Tracking” – p. 5 Fundamentals (4) Tracking objects can be complex due to:  loss of information caused by projection of 3D world on 2D image  noise in images  complex object shapes / motion  nonrigid or articulated nature of objects  partial and full object occlusions  scene illumination changes  real-time processing requirements Simplify tracking by imposing constraints:  Almost all tracking algorithms assume that the object motion is smooth with no abrupt changes  The object motion is assumed to be of constant velocity  Prior knowledge about the number and the size of objects, or the object appearance and shape

Kapitel 7 “Tracking” – p. 6 Object Represention (1) Object representation = Shape + Appearance Shape representations:  Points. The object is represented by a point, that is, the centroid or by a set of points; suitable for tracking objects that occupy small regions in an image  Primitive geometric shapes. Object shape is represented by a rectangle, ellipse, etc. Object motion for such representations is usually modeled by translation, affine, or projective transformation. Though primitive geometric shapes are more suitable for representing simple rigid objects, they are also used for tracking nonrigid objects.

Kapitel 7 “Tracking” – p. 7 Object Represention (2)  Object silhouette and contour. Contour = boundary of an object. Region inside the contour = silhouette. Silhouette and contour representations are suitable for tracking complex nonrigid shapes.  Articulated shape models. Articulated objects are composed of body parts (modelled by cylinders or ellipses) that are held together with joints. Example: human body = articulated object with torso, legs, hands, head, and feet connected by joints. The relationship between the parts are governed by kinematic motion models, e.g. joint angle, etc.  Skeletal models. Object skeleton can be extracted by applying medial axis transform to the object silhouette. Skeleton representation can be used to model both articulated and rigid objects.

Kapitel 7 “Tracking” – p. 8 Object Represention (3) Object representations. (a) Centroid, (b) multiple points, (c) rectangular patch, (d) elliptical patch, (e) part-based multiple patches, (f) object skeleton, (g) control points on object contour, (h) complete object contour, (i) object silhouette

Kapitel 7 “Tracking” – p. 9 Object Represention (4) Appearance representations:  Templates. Formed using simple geometric shapes or silhouettes. Suitable for tracking objects whose poses do not vary considerably during the course of tracking. Self-adapation of templates durch the tracking is possibe.

Kapitel 7 “Tracking” – p. 10  Probability densities of object appearance, can either be parametric (Gaussian and mixture of Gaussians) or nonparametric (histograms) Characterize an image region by its statistics. If the statistics differ from background, they should enable tracking. nonparametric: histogram (grayscale or color) Object Represention (5)

Kapitel 7 “Tracking” – p. 11 Object Represention (6) parametric: 1D Gaussian distribution

Kapitel 7 “Tracking” – p. 12 Object Represention (7) parametric: n-D Gaussian distribution Centered at (1,3) with a standard deviation of 3 in roughly the (0.878, 0.478) direction and of 1 in the orthogonal direction

Kapitel 7 “Tracking” – p. 13 Object Represention (8) parametric: Gaussian Mixture Models (GMM)

Kapitel 7 “Tracking” – p. 14 Object Represention (9) Beispiel: Mixture of three Gaussians in 2D space. (a) Contours of constant density for each mixture component. (b) Contours of constant density of mixture distribution p(x). (c) Surface plot of p(x).

Kapitel 7 “Tracking” – p. 15 Object Represention (10) Object representations are chosen according to the application  Point representations appropriate for tracking objects, which appear very small in an image (e.g. track distant birds)  For the objects whose shapes can be approximated by rectangles or ellipses, primitive geometric shape representations are more appropriate (e.g. face)  For tracking objects with complex shapes, for example, humans, a contour or a silhouette-based representation is appropriate (surveillance applications)

Kapitel 7 “Tracking” – p. 16 Object Represention (11) Feature selection for tracking:  Color: RGB, L ∗ u ∗ v ∗, L ∗ a ∗ b ∗, HSV, etc. There is no last word on which color space is more effective; a variety of color spaces have been used  Edges: less sensitive to illumination changes compared to color features. Algorithms that track the object boundary usually use edges as features. Because of its simplicity and accuracy, the most popular edge detection approach is the Canny Edge detector  Texture: measure of the intensity variation of a surface which quantifies properties such as smoothness and regularity In general, the most desirable property of a visual feature is its uniqueness so that the objects can be easily distinguished in the feature space

Kapitel 7 “Tracking” – p. 17 Object Detection (1) Object detection mechanism: required by every tracking method either at the beginning or when an object first appears in the video  Point detectors: find interest points in images which have an expressive texture in their respective localities  Segmentation: partition the image into perceptually similar regions

Kapitel 7 “Tracking” – p. 18 Object Detection (2)  Background subtraction: Object detection can be achieved by building a representation of the scene called the background model and then finding deviations from the model for each incoming frame. Any significant change in an image region from the background model signifies a moving object. The pixels constituting the regions undergoing change are marked for further processing. Usually, a connected component algorithm is applied to obtain connected regions corresponding to the objects.

Kapitel 7 “Tracking” – p. 19 Object Detection (3) Frame differencing of temporally adjacent frames:

Kapitel 7 “Tracking” – p. 20 Object Detection (4) Bildsequenz: ≈ 5 Bilder/s

Kapitel 7 “Tracking” – p. 21 Object Detection (5) Bildsubtraktion: Variante 1 Schwäche: Doppelbild eines Fahrzeugs (aus dem letzten und aktuellen Bild); Aufteilung einer konstanten Fläche

Kapitel 7 “Tracking” – p. 22 Object Detection (6) Bildsubtraktion: Variante 2 Referenzbild f r (r, c): Mittelung einer langen Sequenz von Bildern

Kapitel 7 “Tracking” – p. 23 Object Detection (7)

Kapitel 7 “Tracking” – p. 24 Object Detection (8) Statistical modeling of background: Learn gradual changes in time by Gaussian, I (x, y) ∼ N(μ(x, y), (x, y)), from the color observations in several consecutive frames. Once the background model is derived for every pixel (x, y) in the input frame, the likelihood of its color coming from N(μ(x, y), (x, y)) is computed.

Kapitel 7 “Tracking” – p. 25 Object Tracking (1)  (a) Point Tracking. Objects detected in consecutive frames are represented by points, and a point matching is done. This approach requires an external mechanism to detect the objects in every frame.  (b) Kernel Tracking. Kernel = object shape and appearance. E.g. kernel = a rectangular template or an elliptical shape with an associated histogram. Objects are tracked by computing the motion (parametric transformation such as translation, rotation, and affine) of the kernel in consecutive frames.  (c)+(d) Silhouette Tracking. Such methods use the information encoded inside the object region (appearance density and shape models). Given the object models, silhouettes are tracked by either shape matching (c) or contour evolution (d). The latter one can be considered as object segmentation applied in the temporal domain using the priors generated from the previous frames.

Kapitel 7 “Tracking” – p. 26 Object Tracking (2) Template Matching: brute force method for tracking single objects  Define a search area  Place the template defined from the previous frame at each position of the search area and compute a similarity measure between the template and the candidate  Select the best candidate with the maximal similarity measure The similarity measure can be a direct template comparison or statistical measures between two probability densities Limitation of template matching: high computation cost due to the brute force search  limit the object search to the vicinity of its previous position; position prediction

Kapitel 7 “Tracking” – p. 27 Object Tracking (3) Direct comparison: between template t(i,j) and candidate g(i,j) Bhattacharyya coefficient between two distributions:

Kapitel 7 “Tracking” – p. 28 Object Tracking (4) Example: Eye tracking (direct grayvalue comparison)

Kapitel 7 “Tracking” – p. 29 Object Tracking (5) Example: Elliptical head tracking using intensity gradients and color histograms

Kapitel 7 “Tracking” – p. 30 Object Tracking (6) Mean-shift tracking (instead of brute force search). (a) estimated object location at time t − 1, (b) frame at time t with initial location estimate using the previous object position, (c), (d), (e) location update using mean-shift iterations, (f) final object position at time t. D. Comaniciu, V. Ramesh, and P. Meer, Kernel-based object tracking. IEEE Trans. Patt. Analy. Mach. Intell. 25, 564–575, 2003