Biblio

Internet speeds and technological advancements have made individuals increasingly concerned about their personal information being compromised by criminals. There have been a slew of new steganography and data concealment methods suggested in recent years. Steganography is the art of hiding information in plain sight (text, audio, image and video). Unauthorized users now have access to steganographic analysis software, which may be used to retrieve the carrier files valuable secret information. Unfortunately, because to their inefficiency and lack of security, certain steganography techniques are readily detectable by steganalytical detectors. We present a video steganography technique based on the linear block coding concept that is safe and secure. Data is protected using a binary graphic logo but also nine uncompressed video sequences as cover data and a secret message. It's possible to enhance the security by rearranging pixels randomly in both the cover movies and the hidden message. Once the secret message has been encoded using the Hamming algorithm (7, 4) before being embedded, the message is even more secure. The XOR function will be used to add the encoded message's result to a random set of values. Once the message has been sufficiently secured, it may be inserted into the video frames of the cover. In addition, each frame's embedding region is chosen at random so that the steganography scheme's resilience can be improved. In addition, our experiments have shown that the approach has a high embedding efficiency. The video quality of stego movies is quite close to the original, with a PSNR (Pick Signal to Noise Ratio) over 51 dB. Embedding a payload of up to 90 Kbits per frame is also permissible, as long as the quality of the stego video is not noticeably degraded.

In most correlation filter target tracking algorithms, poor accuracy in the tracking process for complex field images of the target and scale change problems. To address these issues, this paper proposes an algorithm of adaptive multi-feature fusion with scale change correlation filtering tracking. Our algorithm is based on the rapid and simple Kernel-Correlated Filtering(K CF) tracker, and achieves the complementarity among image features by fusing multiple features of Color Nmae(CN), Histogram of Oriented Gradient(HOG) and Local Binary Pattern(LBP) with weights adjusted by visual evaluation functions. The proposed algorithm introduces scale pooling and bilinear interpolation to adjust the target template size. Experiments on the OTB-2015 dataset of 100 video frames are compared with several trackers, and the precision and success ratio of our algorithm on complex scene tracking problems are 17.7% and 32.1 % respectively compared to the based-KCF.

This paper proposes a method for detecting anomalies in video data. A Variational Autoencoder (VAE) is used for reducing the dimensionality of video frames, generating latent space information that is comparable to low-dimensional sensory data (e.g., positioning, steering angle), making feasible the development of a consistent multi-modal architecture for autonomous vehicles. An Adapted Markov Jump Particle Filter defined by discrete and continuous inference levels is employed to predict the following frames and detecting anomalies in new video sequences. Our method is evaluated on different video scenarios where a semi-autonomous vehicle performs a set of tasks in a closed environment.

Re-drawing the image as a certain artistic style is considered to be a complicated task for computer machine. On the contrary, human can easily master the method to compose and describe the style between different images. In the past, many researchers studying on the deep neural networks had found an appropriate representation of the artistic style using perceptual loss and style reconstruction loss. In the previous works, Gatys et al. proposed an artificial system based on convolutional neural networks that creates artistic images of high perceptual quality. Whereas in terms of running speed, it was relatively time-consuming, thus it cannot apply to video style transfer. Recently, a feed-forward CNN approach has shown the potential of fast style transformation, which is an end-to-end system without hundreds of iteration while transferring. We combined the benefits of both approaches, optimized the feed-forward network and defined time loss function to make it possible to implement the style transfer on video in real time. In contrast to the past method, our method runs in real time with higher resolution while creating competitive visually pleasing and temporally consistent experimental results.

Convolutional Neural Networks (CNN) have shown astonishing results in the field of computer vision. This paper proposes a foreground segmentation algorithm based on CNN to tackle the practical challenges in the video surveillance system such as illumination changes, dynamic backgrounds, camouflage, and static foreground object, etc. The network is trained using the input of image sequences with respective ground-truth. The algorithm employs a CNN called VGG-16 to extract features from the input. The extracted feature maps are upsampled using a bilinear interpolation. The upsampled feature mask is passed through a sigmoid function and threshold to get the foreground mask. Binary cross entropy is used as the error function to compare the constructed foreground mask with the ground truth. The proposed algorithm was tested on two standard datasets and showed superior performance as compared to the top-ranked foreground segmentation methods.

Training a feed-forward network for the fast neural style transfer of images has proven successful, but the naive extension of processing videos frame by frame is prone to producing flickering results. We propose the first end-to-end network for online video style transfer, which generates temporally coherent stylized video sequences in near realtime. Two key ideas include an efficient network by incorporating short-term coherence, and propagating short-term coherence to long-term, which ensures consistency over a longer period of time. Our network can incorporate different image stylization networks and clearly outperforms the per-frame baseline both qualitatively and quantitatively. Moreover, it can achieve visually comparable coherence to optimization-based video style transfer, but is three orders of magnitude faster.

With crimes on the rise all around the world, video surveillance is becoming more important day by day. Due to the lack of human resources to monitor this increasing number of cameras manually, new computer vision algorithms to perform lower and higher level tasks are being developed. We have developed a new method incorporating the most acclaimed Histograms of Oriented Gradients, the theory of Visual Saliency and the saliency prediction model Deep Multi-Level Network to detect human beings in video sequences. Furthermore, we implemented the k - Means algorithm to cluster the HOG feature vectors of the positively detected windows and determined the path followed by a person in the video. We achieved a detection precision of 83.11% and a recall of 41.27%. We obtained these results 76.866 times faster than classification on normal images.

This paper address the problem of shadow detection and removal in traffic vision analysis. Basically, the presence of the shadow in the traffic sequences is imminent, and therefore leads to errors at segmentation stage and often misclassified as an object region or as a moving object. This paper presents a shadow removal method, based on both color and texture features, aiming to contribute to retrieve efficiently the moving objects whose detection are usually under the influence of cast-shadows. Additionally, in order to get a shadow-free foreground segmentation image, a morphology reconstruction algorithm is used to recover the foreground disturbed by shadow removal. Once shadows are detected, an automatic shadow removal model is proposed based on the information retrieved from the histogram shape. Experimental results on a real traffic sequence is presented to test the proposed approach and to validate the algorithm's performance.